Is dry soil planting an adaptation strategy for maize cultivation in semi-arid Tanzania?
Is dry soil planting an adaptation strategy for maize cultivation in semi-arid Tanzania?
0 Kurt Christian Kersebaum
1 Johanna Volk
2 Michelle Bonatti
3 Hohenheim University , Hohenheim , Germany
4 Potsdam Institute for Climate Impact Research (PIK) , Potsdam , Germany
5 Federal University of Campina Grande , Campina Grande , Brazil
6 Leibniz Centre for Agricultural Landscape Research (ZALF) , Müncheberg , Germany
Agriculture has the greatest potential to lift the African continent out of poverty and alleviate hunger. Among the countries in sub-Saharan Africa, Tanzania has an abundance of natural resources and major agricultural potential. However, one of the most important constraints facing Tanzania's agricultural sector is the dependence on unreliable and irregular weather, including rainfall. A strategy to cope with climate uncertainty in semi-arid regions is to proceed with the sowing of the crop before the onset of the rainy season. The advantage is that when the rains start, seeds are already in the soil and can begin immediately the process of germination. The objective of this paper was to assess the effectiveness of dry-soil planting for maize as an adaptation strategy in the context of a changing climate in Dodoma, a semi-arid region in Tanzania. For this assessment, the DSSAT crop model was used in combination with climate scenarios based on representative concentration pathways. A probability of crop failure of more than 80% can be expected when sowing occurs during the planting window (of 21 days) starting on 1st November. The next planting window we assessed, starting on 23rd November (which was still before the onset of rain), presented significantly lower probabilities of crop failure, indicating that sowing before the onset of the rainy season is a suitable adaptation strategy. Results also indicated that, despite not reaching the highest maize grain yields, fields prepared for dry-soil planting still produced adequate yields. The cultivation of several fields using the dry planting method is a strategy farmers can use to cope with low rainfall conditions, since it increases the chances of harvesting at least some of the cultivated fields. We conclude that dry-soil planting is a feasible and valid technique, even in scenarios of climate change, in order to provide acceptable maize yields in semi-arid Tanzania.
DSSAT; Sub-Saharan region; Maize yield; Seed germination; Sowing date; Food security
Swedish University of Agricultural Sciences (SLU), Ulls väg 16,
75007 Uppsala, Sweden
Agriculture has the greatest potential to lift Africa out of
poverty and to alleviate hunger. Given the nature of African
agriculture, where a large proportion of farmers are smallholders
and subsistence-based, it is essential to invest in and develop
accessibility to quality inputs, markets for produce, good soils
and soil management techniques, innovative finance tools and
other resources that are needed for sustained agricultural
(Graef et al. 2017)
The agricultural sector in Tanzania is hampered by low
productivity, poor infrastructure, and a lack of technology,
being dominated by smallholder farmers cultivating average
farm sizes of between 0.9 ha and 3.0 ha. About 70% of
Tanzania’s crop area is cultivated by hand hoe, 20% by ox
plough and 10% by tractor, and almost all is rain fed. The
production of food crops dominates the agricultural economy
This background was confirmed in an extensive survey
carried ou t i n Tanzania by t he Tr an s-SEC Project
(Innovating Strategies to Safeguard Food Security using
Technology and Knowledge Transfer: A People-Centred
(Below et al. 2015; Graef et al. 2014, 2017; Löhr
et al. 2016; Mutabazi et al. 2015)
. The major constraints
facing the Tanzanian agricultural sector are the reduction in the
available labor force, low land productivity due to the
application of poor technologies, and dependence on unreliable and
irregular weather (Mmbaga and Lyamchai 2002). Both crops
and livestock are adversely affected by periodic droughts.
Irrigation holds the key to stabilizing agricultural production
in Tanzania, to improving food security, increasing the
productivity and incomes of farmers, and producing higher
valued crops such as vegetables and flowers
(Evans et al. 2012)
However, some other barriers to improve local food
production and security are not directly tied to a lack of irrigation, but
are related to the availability of farm credit, access to seeds
and fertilizers, quality of seeds, farm implements and market
(Mdemu et al. 2017)
One of the major uncertainties with the future trajectory of
agricultural productivity in Africa is the likely impact of a
(IPCC et al. 2007)
. Several studies
(Kurukulasuriya et al. 2006; Lesch et al. 2005; Nelson et al.
2010; Seo et al. 2009; Thornton et al. 2006)
evidence that predicted changes in temperature and rainfall
caused by global warming may impose additional serious
constraints on agriculture in Africa
(Benin et al. 2016)
change will have a direct impact on the availability of water
for irrigated crops. In addition to changes with precipitation,
climate-change-induced higher temperatures increase the
water requirements of crops
(Nelson et al. 2009)
Climate change is likely to make matters worse with further
increases in rainfall variability being predicted
(Agrawala et al.
2003; Schlenker and Lobell 2010)
. Climate change is also
expected to alter pest and disease outbreaks, increase the
frequency and severity of droughts and floods, and increase the
likelihood of poor yields, crop failure and livestock mortality
(Harvey et al. 2014). The ability of agricultural communities
and other agricultural stakeholders in sub-Saharan Africa to
cope with the constraints and opportunities of current climate
variability must first be enhanced in order to be able to adapt to
climate change and the predicted future increase in climatic
variability. Tools and approaches are now available that allow
for a better understanding, characterization and mapping of the
agricultural implications of climate variability and the
development of climate risk management strategies specifically
tailored to the needs of stakeholders
(Cooper et al. 2008)
Specific concerns about climate variability include
variability in the onset and cessation of rainfall, rainfall amount,
and frequency and duration of periods of soil water deficits.
This variability greatly affects crop yield in rainfed systems,
and is a major disincentive to the adoption of yield-improving
practices, which then challenges researchers to develop
adoptable practices and varieties. Therefore, knowledge of the
seasonal climate variability and its associated risks is important to
the improvement of crop management
(Liben et al. 2015)
The shifting of planting date based on cultivar properties
could prevent critical stages of crop development from
coinciding with periods of extreme high temperature and water
deficit, consequently helping to reduce yield loss from climate
(Tao and Zhang 2010)
. Generally, crop yields may
suffer substantially with either a late onset or early cessation
of the growing season. Shifting of planting date ensures that
the seed, once in the soil, has sufficient moisture to trigger the
germination process and further crop development. However,
in order to be successful and effective this strategy needs the
farmers to be available all the time before the expected onset
(Mugalavai et al. 2008)
Under the conditions observed in rural communities of
Tanzania, especially in the semi-arid regions, farmers usually
farm several field areas, sometimes many kilometers apart; the
principle behind this is to take advantage of better soils and
reduce the risk of total crop failure associated with the
smallscale spatial distribution of precipitation. To be able to use
fields in different areas, waiting for the onset of rainfall is
not an adequate strategy. Therefore, farmers often practice
so-called ‘dry soil planting’, when they seed the crop into
dry soil a few days before the expected start of the rainy
. The advantage of this practice is that
when the rainfall starts seeds are already in the soil and can
immediately start to germinate. This is especially important in
regions where the rainy season is not long enough to provide
adequate moisture during the whole cropping cycle.
As an example, according to
, in Mvumi,
Tanzania, planting activities are normally carried out some
two to three weeks before the expected time for the onset of
the rains; in that study villagers explained that early planting is
done prior to the onset of the rains so that crops could benefit
from all of the moisture provided during the short rainy
season. The same author, citing Holtland (1994), explains that
seedlings make optimal use of the natural nitrogen (N)-flush
that occurs after the first rain-showers. As N availability is,
after water, the greatest constraint to cultivation in the
Dodoma region, the difference between dry planting and
planting a week after the onset of the rains can be
Nevertheless, despite the advantage of a faster start,
planting into dry soil also poses the risk that germination is initiated
by a precipitation event that is not the start of a rainy season. In
this situation, the crop can start to germinate but then die
during subsequent drying of the soil and seedling. In other
situations there is also a risk that if the seed stays for a long
period in the soil without sufficient moisture to trigger
germination, high temperatures can cause loss of vigor, or it can be
damaged or eaten by insects or other animals
(Benin et al.
2016; Cooper et al. 2008)
In addition to planting dry seeds, seeds can be ‘primed’
with water, an enhancement method that might improve
seed performance under stress conditions such as drought
or when freshly harvested or aged seeds are used which
l to germinate (Lutts et al. 2016
Harris et al.
demonstrated how simple soaking seeds in water
before sowing can increase the speed of germination and
emergence, leading to better crop stands, and allow
seedlings to grow much more vigorously. Some farmers in
Zimbabwe already have experience of soaking maize
and sorghum seeds in water before sowing, in an attempt
to improve establishment, but the practice appears to be
neither widespread nor regularly followed, probably
because farmers need the opportunity to experiment for
themselves; to do their own research and development
(Harris et al. 2001)
Maize is the staple food for the majority of Tanzanians,
providing about 60% of Tanzanian’s dietary calories and
50% of the protein. With about five million ha, Tanzania has
the largest planted area of maize in all Southern and East
Africa. Maize production has significantly increased over the
past 10 years, largely through expansion of area planted rather
than increased grain yields. Over the past 50 years, maize
production has kept pace with the increase in population
Most maize (80%) is produced by small-scale farmers and
is grown both for subsistence and as a cash crop. Between
65% and 80% of all maize is consumed within the producing
households: only 20% to 35% enters commercial channels.
Maize comprises an average of 16% of national household
food expenditures, though there are big regional variations.
The choice to grow maize, even in areas of insufficient
rainfall, is driven by a strong dietary preference for maize over the
more drought-adapted traditional cereals such as sorghum and
millets. Efforts are being made to develop more drought
tolerant cultivars of maize available to Tanzanian farmers and to
(Doebley et al. 2010)
Most maize production in Tanzania is under low-input
rainfed conditions. Simple hand hoes, farm-recycled seed,
little use of synthetic fertilizers or agrochemicals and minimal
weeding are the usual technologies and inputs employed.
Maize crops frequently fail because of insufficient soil
moisture. Irrigation is not usually available or selected as an option
for maize, and on-farm water harvesting techniques are not yet
well known or used
(Wilson and Lewis 2015)
Drought is a major threat to maize in many parts of
Tanzania. Maize production can be a risky and unreliable
business because of erratic rainfall and the high susceptibility
of maize to drought, while the performance of local
droughttolerant cultivars is poor
(Mmbaga and Lyamchai 2002)
Erratic rainfall is making maize farmers in Tanzania
vulnerable to low yields, which translates to food insecurity. Tanzania
suffered the effects of a prolonged drought in the recent years
(Doebley et al. 2010)
. These observations agree with the
information gathered from farmers in the region during a
comprehensive survey about many aspects of food security in the
region (see Trans-SEC: Innovating Strategies to Safeguard
Food Security using Technology and Knowledge Transfer:
A People-Centred Approach - www.trans-sec.org).
In order to assess and understand the effects of climate
change, crop models can be a useful tool to assess the
influence of climatic and other environmental or management
factors on crop development and yield
(Reidsma et al. 2010)
Decision Support System for Agrotechnology Transfer –
DSSAT v. 4.5 contains the Crop System Model CERES –
(Jones et al. 2003)
and can help to a) determine
best planting dates, b) define fertilizer timing and amounts, c)
support precision agriculture and d) detect/investigate
potential impacts of climate change on agriculture. In the embedded
CERES – Maize model, the development and growth of the
crop is simulated on a daily basis from planting until
physiological maturity. The model calculations are based on
environmental and physiological processes that control the phenology
and dry matter accumulation in different organs of the plant.
The DSSAT also has other embedded models that can simulate
the flow of nutrients and water balance in the soil. Despite the
complex array of processes simulated by the crop model,
some important processes such as effects of pests and diseases
are still not well depicted
(Donatelli et al. 2017)
, as are the
impact of extreme events such as floods and hail
and Hoogenboom 2000)
or high temperatures (above 35 °C)
during anthesis, which reduces pollen viability
Considering that smallholder farmers in Tanzania cultivate
several fields at the same time, of which only some may be
under a dry-soil planting management system, it can be
expected that dry-soil planting is and will still be part of the
strategies to reduce the risk of crop losses and help ensure
food security in the context of a changing climate. Based on
that, the objective of this paper was to assess the effectiveness
of dry-soil maize planting as an adaptation strategy in a
semiarid region of Tanzania. This assessment was done using a
calibrated CERES – Maize crop model and tested different
climate scenarios for the 2020–2059 and 2060–2099 periods.
Our assessment was done for the Dodoma region of Tanzania,
as part of the activities of the Trans-SEC Project
et al. 2017)
. The study region lies between latitudes 5°50′S to
6°10′S and between longitudes 35°40′E to 36°05′E, at an
elevation of 1020 m above sea level. The dominant soil of the
target region is defined as a ferralic Cambisol (FAO), which is
low in fertility and seasonally waterlogged or flooded
(Msongaleli et al. 2015)
. Detailed information about the soil
used in this assessment is presented in Table 1. The area is
characterized by low and erratic rainfall with a unimodal
rainfall regime. The long-term mean annual rainfall is about
511 mm with average temperatures of 22.7 °C (Fig. 1). The
onset of rainfall usually occurs in early-mid December, and
the rainy season extends until April. As the rainfall pattern in
the study region is variable within the rainy season, maize
cultivation is restricted to areas where water availability is
higher and the soil has a higher water holding capacity.
2.2 Climate scenarios
Data for future climate scenarios are available from the
InterSectoral Impact Model Intercomparison Project – ISI-MIP
(Warszawski et al. 2014) as a bias-corrected dataset with daily
values for temperature (Tmax, Tavg, Tmin), precipitation,
relative humidity, and solar radiation. These data are well
accepted and also used for the Agricultural Model Intercomparison
and Improvement Project – AgMIP
(Rosenzweig et al. 2013)
The selected Representative Concentration Pathways (RCPs)
for our study were the high-emission scenario RCP8.5, the
medium-low-emission scenario RCP4.5 and the
lowemission scenario RCP2.6 scenario
(Moss et al. 2010)
each RCP scenario, five different projections (model-derived
estimates of future climate) were used, as follows:
(Dunne et al. 2013)
(Johns et al.
(Hourdin et al. 2013)
(Watanabe et al. 2011)
, and NorESM1-M
(Iversen et al. 2012)
, totaling 15 different projections of three
RCPs. All the models have the added capability of explicitly
representing biogeochemical processes that interact with
physical-climate processes. To capture the fertilization
effect of increasing levels of atmospheric CO2
(Long et al.
2006; Tubiello et al. 2007)
, the concentration of CO2 was
a d j u s t e d y e a r l y a c c o r d i n g t o t h e r e s p e c t i v e R C P
(Meinshausen et al. 2011).
2.3 Crop modeling
To generate projections of maize yield and assess the impact
of weather scenarios and dry-seed planting management, a
crop simulation model was employed. The crop modeling
process was done using DSSAT
(Jones et al. 2003)
established crop model already used in several impact
(Rivington and Koo 2011)
The maize cultivar used in this study was a locally adapted,
open pollinated cultivar called Situka, with a yield potential at
around 4 t ha−1 in Dodoma (according to reports from local
technicians), and commonly cultivated by local farmers
because of its early maturity and tolerance to low levels of soil
nitrogen (N), a typical condition of the study area
et al. 2015)
. This cultivar was already calibrated and validated
(Mourice et al. 2014a, b)
for Morogoro, situated
250 km east from Dodoma, where it can reach yields of 6.6 t
ha−1. However, for this study, a further validation step was
done using three years of field data including soil, weather,
agronomic management and yield
(Kimaro et al. 2008, 2009)
Tmax and Tmin baseline
from research experiments conducted in nearby villages to
confirm the ability of the model to simulate past observations
in the study region. Difference-based indices such as mean
bias error (MBE)
(Addiscott and Whitmore 1987)
percentage error (MPE), root mean squared error (RMSE)
and relative root mean squared error (RRMSE)
Jørgensen et al. 1986
) were used to evaluate the simulation
outputs. The final maize plant population density was defined
at 33000 plants per ha, planted at 7 cm depth in rows with
75 cm spacing between rows. This low plant population
density, the space between rows and planting depth represent the
common practices in the region. Fertilizer use was set to
2000 kg ha−1 of dry cattle manure, equivalent to 40 kg ha−1
N, to be applied on the day of planting. No irrigation was
applied. DSSAT was instructed to start the simulations on 1
January each year to provide a more realistic soil water
balance at sowing. For planting dates taking place after 1 January,
the simulation started on 1 January of the previous year.
Harvest was set to take place two weeks after physiological
maturity, as calculated by the model. The format of climate
scenario files was adjusted for DSSAT structure using the
(Wilkens et al. 2004)
. Simulations were
run for each projection and RCP. For the analysis of results,
yields generated with different projections were merged in
ensembles (according RCP) in order to provide more robust
(Randall and Wood 2007)
2.4 Adaptation strategies
Initially, in order to establish a yield baseline, simulations
using all the RCPs were run for the 2000–2015 period using
the different planting windows (described in the next
paragraph), starting on 1st of November and at every 21 days, until
1st of February, totaling seven planting windows. The
objective was to verify if the simulations could mimic the best
planting window (mid-December) and crop management as
informed by the local farmers and technicians involved in the
Trans-SEC project. Yields from the same planting window
were averaged to identify the one with the highest yields (best
planting date). Once the best planting window was defined for
the baseline period, the next step was to run simulations using
this planting window for the 2020–2059 and the 2060–2099
In order to mimic the actual practice in the region of dry
soil planting (that means that farmers plant the seeds into dry
soil and wait for rainfall onset within 21 days), five planting
windows (each of 21 days) were defined, starting on 1
November. The assumptions were that 1) in the dry soil
planting systems the seed would remain viable for 21 days and
initiate the germination process only if the soil moisture of
the top 20 cm reached at least 30% of the soil field capacity,
and that 2) based on soil texture and soil bulk density the soil
module embedded in the crop model was able to correctly
simulate the soil moisture. Although maize seed can absorb
moisture from the soil even when soil moisture conditions are
far below permanent wilting point
(Muthukuda Arachchi et al.
, the minimum external water potential permitting maize
germination observed by
Hunter and Erickson (1952)
shown to be −1.25 MPa. The same authors reported that maize
seed started to germinate only when seed moisture exceeded
the critical value of 30.5% of field capacity. If during the
planting window the critical soil moisture was not reached,
it is assumed that the seeds wereno longer in a condition to
germinate, and the yield was set to zero. If germination starts,
but a dry spell kills the seedling (crop abortion) or the crop
produces less than 400 kg ha−1 of grain, then the crop season
was considered lost (crop failure).
3.1 Model and cultivar validation
As the crop model and the Situka cultivar were already
validated for a different region of Tanzania
(Mourice et al.
, but not for a nearby location, a second validation
was considered necessary. Simulations using the same
conditions (real data for weather, soil, agronomic management)
of the field experiments done by Kimaro et al. (2008, 2009)
including a nearby region, were done. The results of the
validation were satisfactory, indicating that the model could
mimic maize grain yields from field observations of three
available crop seasons (2004–2006), as presented in
Table 2. As the MBE term describes the direction of the
error bias, the positive value of 160 kg ha−1 indicates that
the predictions were overestimated (in absolute values)
compared to the observations. If all simulated and observed
values are the same, then MBE, MPE, RMSE and RRMSE
should be equal to zero. Overall, the results certified that the
model could adequately mimic field observations for maize
yield. Nevertheless, it is important to emphasize that the
experiments used to generate these observations were
village-level research experiments, and not data gathered
from maize crops on smallholder farm fields, where yields
are probably often lower. Data related to phenology and
above ground biomass were not available for comparison
in this exercise.
3.2 Definition of best actual planting dates
Simulations run for the 2000–2015 period using the data for the
RCPs identified the planting windows beginning on the dates
12 December and 6 January as the ones with the highest grain
yields (Fig. 2). This coincided with the onset of the rainy season
as reported by the local farmers from the study sites (Fig. 1).
3.3 Climate change scenarios
For Tanzania, and in particular for the Dodoma region, the
RCP’s showed slight changes in the precipitation amount
during the crop season (Fig. 3). No significant shift in the onset of
the rainy season was identified for the study region. For the
2020–2059 period, the scenarios indicated a stability or
reduction in accumulated precipitation until February–March (using
the 2000–2015 period as baseline). For the rest of the crop
Table 2 Results of the validation process for the DSSAT crop
simulation model using difference-based indices comparing simulated
and field observations of grain yields from maize cv. Situka in three
different seasons (2004–2006) in Tanzania. MBE: mean bias error;
MPE: mean percentage error; RMSE: root mean squared error;
RRMSE: relative root mean squared error
MBE, Mean Bias Error; MPE, Mean percentage error; RMSE, Root Mean
Squared Error; RRMSE, Relative Root Mean Squared Error
season, the RCPs tended to deviate in terms of accumulated
precipitation. For the second period (2060–2099), the models
projected a slight reduction in accumulated rainfall at the
beginning of the cropping season, with later increments in
precipitation in the 4.5 and 8.5 RCPs. In contrast to projections
for other regions of the world, the precipitation pattern in
Tanzania should not change its timing, especially for the first
period (2020–2059). In terms of the amount of rainfall, the 4.5
and 8.5 RCPs indicated an increase in precipitation volume for
the later period (2060–2099), when compared to the baseline
(Fig. 3b), while the RCP 2.6 indicated a reduction in
For air temperature (Fig. 4), all RCP’s indicated an increment
for Tmax and Tmin. For the first time slice, the three RCPs
showed relatively similar increments in both Tmax and Tmin.
For the 2060–2099 period, there was a clear distinction between
the different RCPs, with the highest increase observed for the
RCP 8.5, followed by the RCP 4.5 and finally the RCP 2.6.
3.4 Impact assessment with the use of dry soil
When using dry-soil planting as a strategy to make possible
the cultivation of several different fields per farm, we assessed
that the very early dry soil planting window (starting date 1
November) for both time periods (2020–2059 and 2060–
2099) presented more than an 80% probability of failure for
all RCPs, while the next planting windows reduced the
probability of failure to less than 20% (Fig. 5a and b). In the best
situation, when planting occurs near mid-December, the
chances of losses from crop failure are very low. The dry soil
planting practice in the selected region can only be said to
occur when done before mid-December. After this planting
window, with the very likely onset of the rainy season, sowing
will take place into already wet soil.
Accum. Prec. Baseline
Accum. Prec. RCP 2.6
Accum. Prec. RCP 4.5
Accum. Prec. RCP 8.5
Fig. 4 Maximal (Tmax) and minimal (Tmin) temperature changes in Dodoma, Tanzania according different representative concentration pathways
(RCPs) for two time periods (2020–2059 and 2060–2099). The dashed line represents the baseline relating to the 2000–2015 period
Fig. 5 Frequency of maize crop failure (defined as grain yields lower than
400 kg ha−1) derived from all representative concentration pathways
(RCPs) according different planting windows (each of 21 days) using dry
soil planting as a strategy in Dodoma, Tanzania. Values for the 2020–2059
period are on the left (a), while the 2060–2099 period are on the right (b).
The date on the x axis indicates the start of the planting window
third planting window also does not meet the minimal soil
moisture levels (30.5% of field capacity) at the beginning, as
seen by the high dispersion of symbols in Fig. 6 indicating the
start of the germination process. By advancing the planting
window, the start of germination tends to concentrate on or
very close to the beginning of each planting window, because
of sufficient soil moisture to trigger germination. Especially
for the two last planting windows, in both time periods, we
observed that for almost all RCPs and years, the soil already
had enough moisture for immediate seed imbibition and the
start of germination. This higher soil moisture availability at
the planting time, however, does not automatically ensure
good crop performance, as yield will also be defined by
climatic events that occur well after germination.
For maize grain yield, the most successful planting
windows for both time slices were between 23 November and 6
January, as seen in Fig. 7. As yield is defined by several
parameters, it is assumed that the above mentioned planting
windows were the ones combining the best emergence
conditions for the crop (soil moisture) as well as adequate water
supply during the crop’s growth cycle. Late planting date
windows, despite the high values of initial soil moisture that allow
for immediate germination, failed to provide an adequate
amount of rainfall during the vegetative stages and especially
during the reproductive stages of maize development, which
occur towards the end of the rainy season. As seen in Fig. 3,
precipitation ceases in April, when the plants that germinated
during the latest planting times are expected to be in the grain
Fig. 6 Maize grain yields simulated by the DSSAT model using different
scenario periods and different planting windows (dates in the X axis
indicate the start of the planting window) using the dry soil planting
adaptation strategy for Dodoma, Tanzania; (a) 2020–2059; (b) 2060–
2099. The different representative concentration pathways (RCP’s) are
represented by ○ (RCP 2.6), ● (RCP 4.5) and ▼ (RCP 8.5) and
indicate when the critical threshold of soil moisture for seed imbibition
filling phase. Comparing the baseline yields (for 2000–2015)
and projected yields for 2020–2099 from both time periods
(slices) (Fig. 7), no large changes in yield were expected to
occur under both time slices.
In cropping systems reliant on rainfall as the sole source of
moisture for crop growth, seasonal rainfall variability is
inevitably mirrored in both highly variable crop production levels
and in the risk-averse livelihood and coping strategies that
have emerged over time in smallholder farming communities.
For this assessment, the Ceres crop model evaluation using
the Situka maize cultivar was considered adequate since values
of MPE and RRMSE are close to zero. This shows that, when
input parameters are known, the model can properly mimic field
observations such as the ones of Kimaro et al. (2008, 2009) used
in this exercise for model evaluation. The RRMSE below 10%
can be considered excellent, according
Jamieson et al. (1991)
The RMSE (204 kg ha−1) provides the square root of the average
of squared differences between prediction and actual
observations, while the RRMSE indicates as a percentage how
heterogeneous the simulation results are in relation to the observations.
The difference between the MPE and the RRMSE (1.7%)
shows that, by using the RMSE as the base component, errors
are magnified. Also a positive MBE is desirable, indicating that
the model over-predicted yields, according
Another aspect that supports the ability of the crop model to
mimic field observations is that the simulations using weather
scenarios for the period 2000–2015 matched the best planting
dates reported by farmers in the study region.
For temperatures in East Africa, models project increments in
surface air temperature over land between the end of the century
(2070–2099) and the present day (1980–2010). Similarly, for
annual precipitation, all models indicate an increase of 5% to
20% in precipitation (Warszawski et al. 2014). These different
future climate scenarios allowed the ‘mapping’ of the
uncertainty of impact assessments on crop yields and have already been
used in several other assessments as listed by
Müller et al.
, but with different levels of precision.
Despite the significant increases in temperature predicted for
the later time period (slice) (2060–2099) (Fig. 4), these
deleterious effects could be neutralized or partially reduced by the
increment in precipitation (Fig. 3) observed in the middle of the
cropping season. An increment in temperature will also
accelerate the development of the crop, which may help to avoid the
occurrence of the grain filling phase after the end of the rainy
season. Additionally, the viability of maize pollen decreases with
exposure to temperatures above 35 °C
(Dupuis and Dumas
, and this effect can be enhanced under high vapor pressure
(Hatfield and Prueger 2015)
. It is important to note that
there is a differentiation between the RCPs only in the 2060–
2099 period (Fig. 7b), when RCP 4.5 projected the highest
yields. For this RCP, the significant increment in temperature
was neutralized by the increment in accumulated precipitation.
Data from a field survey carried out by Mourice et al.
(2014a, b) for the Wami Basin (which includes the study
region) shows that reported yields of maize on smallholder
farms range from 50 kg ha−1 to 3600 kg h−1, with an average
of only 860 kg ha−1. The discrepancy observed between the
yields from Table 2 (which originated from field experiment
observations and simulations) and the ones reported above can
be attribute to many factors, ranging from agronomic
management (sowing date, plant population, fertilizer use, cultivar,
irrigation), biophysical factors (soil type, soil compaction,
fertility, water or temperature stress, attack of pests, weeds,
diseases) to other problems such as animal grazing, theft and
lodging. Specifically with regard to crop models an important
aspect to be considered is that under field conditions crops will
often be subjected to stressors that are not included or not
simulated in the model (e.g. pests, diseases, and many others).
As stated by
Donatelli et al. (2017)
, this capability to fully
model the farm situation is still missing in many crop models,
although developments in recent decades are moving towards
the quantitative description of the impact of pests and diseases
on yield. Differences in management (cultivar, plant
population, planting date and soil and nutrient management), soil and
weather also contribute to increase this gap.
Luhunga et al.
, using a fixed CO2 concentration of 360 ppm, also
assessed the impacts of climate change scenarios on maize
production in the Wami river basin of Tanzania using a single
planting window of 15 days (1–15 December), reaching
slightly lower baseline yields for maize. Both studies used
the same maize cultivar (Situka), but without conducting a
further calibration to verify the plausibility of the model for
other regions, as recommended by
Grassini et al. (2015)
Since the region where the crop model was applied does have
diverse environmental conditions, it is important to test,
adjust, and validate model parameters as necessary.
Regarding dry-soil planting as an adaptation strategy,
Akinnagbe and Irohibe (2014)
reported that in Tanzania, to
avoid crop production risks due to rainfall variability and
drought, staggered planting into dry soil before the onset of
rains is very common for many farmers. The cultivation of
several fields using the dry planting method is therefore
regarded as a special feature of adapting to low rainfall
conditions since it increases the chances of harvesting at least
some of the cultivated fields.
Another aspect that favors dry-soil planting as an ex-ante
drought risk adaptation strategy to help ensure food security is
to shift and extend the cultivation area to different fields
(Westengen and Brysting 2014)
. The spatial diversification
of fields is an effective ex-ante drought risk coping strategy
that reduces exposure to risk
(Shiferaw et al. 2014)
such conditions, the risk of income shortfall is reduced by
growing maize crops on several fields, and reduced even more
when different crops are cultivated. According to
Pandey et al.
, this principle is used in different types of
diversification common in rural societies. Examples include the spatial
diversification of farms, diversification of agricultural
enterprises and diversification from farm to non-farm activities.
Although ex-ante strategies can be costly in terms of
foregone opportunities for income gains because of the choice of
safer but low-return activities by farmers, they might help with
reducing fluctuations of income
(Pandey et al. 2007)
reduce food insecurity. Moreover, since the availability of labor
is one of the major factors affecting the performance of
agriculture, the cultivation of some plots before the rainy season
maximizes labor efficiency. In this situation, farmers prepare and
cultivate the most distant fields before the expected start of the
(Graef and Haigis 2001)
, leaving the cultivation of
the closest fields for the beginning of the rainy season.
Despite the potential to reduce crop losses and therefore to
affect food security, the employment of dry-soil planting
needs to be followed by other initiatives to reduce mining of
(Bekunda et al. 2004)
, improvement of soil
water retention potential, the reduction of soil erosion,
diversification of crops, among others. Practices to design
climatechange-resilient farming systems are comprehensively
Altieri et al. (2015)
and can foster the local food
security by diluting the risk of total crop loss, reducing the
variability in yields and ensuring a source of income.
Planting into dry soil is an important strategy for farmers
managing several fields under circumstances of low water
availability. The main advantage of this practice is the planting
before the onset of the rainy season, while the main drawback
is that a delay in the onset of the rainy season will increase the
risk of a false start or crop failure.
For this impact assessment, a crop model (the Ceres model
in DSSAT) was satisfactorily validated for a maize cultivar
(Situka) in the Dodoma region of Tanzania, indicating its
ability to respond to different planting dates and climate change
The climate change scenarios did not indicate a
considerable shifting in the onset and end of the rainy season, but
changes in the accumulated precipitation during the cropping
season were predicted for the 2060–2099 period. For
temperature, an increment in maximum temperature during the
cropping season for the 2020–2059 period of about 1 °C
was predicted for all representative concentration pathways
(RCP’s), while for the 2060–2099 period increments ranged
from <1 °C to almost 3 °C, depending on the RCP.
Regarding the probability of failure of the maize crop
(yields <400 kg ha−1), early planting windows (beginning 1
November) presented up to an 85% chance of failure, with the
lowest failure probability occurring with the 15 December
planting window. The model simulation results indicated that
fields prepared and sowed under the concept of dry-soil
planting (no more than 21 days before the onset of the rainy season)
had a considerably lower probability of crop failure for the
two different tested periods (2020–2059 and 2060–2099).
We conclude that dry-soil planting is a feasible adaptation
strategy for farmers to cultivate more fields with maize and
therefore reduce the risks of food insecurity in the Dodoma
region of Tanzania.
Acknowledgements All the activities related to this work were done with
support of the Trans-SEC Project, sponsored by the German Federal
Ministry of Education and Research (BMBF) and co-financed by the
German Ministry for Economic Cooperation and Development (BMZ).
The first author also expresses his gratitude to the MACSUR Knowledge
HUB (CropM, C1).
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of
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Ana Carolina F. Vasconcelos
holds a degree in Agricultural
Engineering and a Masters in
Development and Environment
from the Federal University of
Paraíba, Brazil. She holds a PhD
in Agronomy (Soil and Plant
Nutrition) from the University of
São Paulo. Ana has experience in
t h e f i e l d o f A g r i c u l t u r a l
Engineering, with emphasis on
Soil Fertility and Plant Nutrition,
working mainly on salinity,
climate change, and adaptation
strategies to climate change.
Christoph Gornott has a Masters
of Science and is currently a PhD
student at the Potsdam Institute
for Climate Impact Research
(PIK) in Germany. He is
completing his PhD thesis about regional
yield assessments by using
statistical and process-based crop
models. He is experienced in
project management in several
ongoing projects. Within the
TransSEC (BMBF, BMZ) project, he
has four years of experience with
crop modelling and food security
assessment. At PIK, Christoph
has worked on regional crop yield modelling, climate impacts and
improvement of agricultural production systems, as well as links between
crop field trials and crop modeling. He also has a masters degree in
agricultural economics and a bachelors degree in agricultural science
(both from the Humboldt University Berlin).
Angela Schaffert holds a Masters
Degree in Agricultural Sciences
in the Tropics and Subtropics.
Currently she is a PhD student
w i t h t h e U n i v e r s i t y o f
Hohenheim in Germany. Her
ongoing research activities focus on
crop water use efficiency in
drought-prone areas in Tanzania,
with an emphasis on the effect of
tied-ridges on soil water
conservation. Previously, she did
research in southern Ethiopia on
the potential of carbon
sequestration in savannahs.
J o h a n n a V o l k i s a B S c
Geographical Sciences student at
the Freie Universität Berlin. She
is currently working on her thesis
in association with the Centre for
Agricultural Landscape Research
(ZALF) on the impacts of climate
change scenarios on rain fed
maize production in a sub-region
of Tanzania. Johanna is acquiring
research skills in crop modelling
in order to simulate and analyze
future agricultural outputs under
different scenarios of climate
change. During her studies, she
engaged in issues related to food security through a physical and human
geography perspective, with an emphasis on soil science and
development studies. A fellowship from the German Academic Exchange Service
(DAAD) made it possible for her to spend a semester abroad at
Stellenbosch University, South Africa and work for a local NGO, the
Women on Farms project.
(BfN) in Bonn, Germany, where he was involved in the regulation of
genetically modified organisms (GMO). There his special focus was on
cultivation systems, and strategies for monitoring their potential effects on
biodiversity and ecosystems.
Kurt Christian Kersebaum
works as senior scientist at the
Institute of Landscape Systems
Analysis from the Leibniz Centre
for Agricultural Landscap e
R e s e a r c h – M ü n c h e b e r g ,
Germany. He is an agricultural
engineer with a PhD in horticulture
a n d h o l d s a D r. H a b i l i n
geoecology from the University
of Potsdam. His main expertise
is in agro-ecosystem modelling
with a focus on water and matter
dynamics, crop growth and
climate change. He has authored or
co-authored more than 180 scientific works, is the national delegate for
Germany in the management committee of COST ES 1106 EU
(AGRIWAT Assessment of EUROpean AGRIculture WATer use and
trade under climate change), is editorial board member of several
scientific journals and associated with different scientific associations. He also
received the R. Ahuja Ag. Systems Modeling Award from the American
Soil Science Society.
Stefan Sieber is an agricultural
economist working as a senior
scientist and head of the
departm e n t o f E c o n o m i c s o f
Sustainable Land Use at the
Centre of Agricultural Landscape
Research (ZALF). He holds a
PhD in agricultural sciences from
the University of Bonn. He has
extensive experience in
agricultural sector modelling,
particularly in terms of impact assessment
of environmental and
sustainability policies and in applied
monitoring and evaluation methods of
international development projects worldwide (Europe, Latin America
and Africa). Stefan has managed more than 25 research projects and
has authored or co-authored more than 35 peer-reviewed publications,
12 peer-reviewed contributions for books and research series, as well as
80 conference papers. He is currently completing his second PhD
(Habilitation) and is a lecturer on the master’s program BEnvironmental
Sociology and Environmental Policy^ at the Humboldt University of
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Wilson , R. T. , & Lewis , J. ( 2015 ). The MAIZE value chain in tanzania. Food and Agriculture Organization of the United Nations (p. 60 ). Rome, Italy. http://www.saiia.org.za/value-chains-in-southernafrica/ 1055 -008 -tanzania-maize/file Marcos Alberto Lana holds a degree in Agronomy and a Masters in Agro-ecosystems from the Federal University of Santa Catarina State, Brazil. He holds a D o c t o r a l d e g r e e i n C r o p Production from the ChristiansA l b r e c h t U n i v e r s i t ä t - K i e l , Germany. Currently Marcos works as scientist and lecturer at t h e S w e d i s h U n i v e r s i t y o f Agricultural Sciences in Uppsala, and as a visiting scientist at the Centre of Agricultural Landscape Research (ZALF), Müncheberg,