Content uploaded by Prakash N. Dixit
Author content
All content in this area was uploaded by Prakash N. Dixit on Sep 24, 2014
Content may be subject to copyright.
Expl Agric. (2011), volume 47 (2), pp. 317–338 C
Cambridge University Press 2011
doi:10.1017/S0014479710000773
ADDING VALUE TO FIELD-BASED AGRONOMIC
RESEARCH THROUGH CLIMATE RISK ASSESSMENT:
A CASE STUDY OF MAIZE PRODUCTION
IN KITALE, KENYA
By P. N . D I X I T †,‡,P.J.M.COOPER†, J. DIMES§ and K. P. RAO†
†International Crops Research Institute for the Semi Arid Tropics (ICRISAT), P.O. Box 39063,
Nairobi 00623, Kenya and §International Crops Research Institute for the Semi Arid Tropics
(ICRISAT), P.O. Box 776, Bulawayo, Zimbabwe
(Accepted 9 September 2010)
SUMMARY
In sub-Saharan Africa (SSA), rainfed agriculture is the dominant source of food production. Over the past
50 years much agronomic crop research has been undertaken, and the results of such work are used in
formulating recommendations for farmers. However, since rainfall is highly variable across seasons the
outcomes of such research will depend upon the rainfall characteristics of the seasons during which the
work was undertaken. A major constraint that is faced by such research is the length of time for which
studies could be continued, typically ranging between three and five years. This begs the question as to
what extent the research was able to ‘sample’ the natural longer-term season-to-season rainfall variability.
Without knowledge of the full implications of weather variability on the performance of innovations being
recommended, farmers cannot be properly advised about the possible weather-induced risks that they
may face over time. To overcome this constraint, crop growth simulation models such as the Agricultural
Production Systems Simulator (APSIM) can be used as an integral part of field-based agronomic studies.
When driven by long-term daily weather data (30+ years), such models can provide weather-induced risk
estimates for a wide range of crop, soil and water management innovations for the major rainfed crops of
SSA. Where access to long-term weather data is not possible, weather generators such as MarkSim can be
used. This study demonstrates the value of such tools in climate risk analyses and assesses the value of the
outputs in the context of a high potential maize production area in Kenya. MarkSim generated weather
data is shown to provide a satisfactory approximation of recorded weather data at hand, and the output of
50 years of APSIM simulations demonstrate maize yield responses to plant population, weed control and
nitrogen (N) fertilizer use that correspond well with results reported in the literature. Weather-induced risk
is shown to have important effects on the rates of return ($ per $ invested) to N-fertilizer use which, across
seasons and rates of N-application, ranged from 1.1 to 6.2. Similarly, rates of return to weed control and
to planting at contrasting populations were also affected by seasonal variations in weather, but were always
so high as to not constitute a risk for small-scale farmers. An analysis investigating the relative importance
of temperature, radiation and water availability in contributing to weather-induced risk at different maize
growth stages corresponded well with crop physiological studies reported in the literature.
INTRODUCTION
In sub-Saharan Africa (SSA) where rainfed agriculture is the dominant source of food
production accounting for nearly 90% of staple food production (Rosegrant et al.,
2002), there has been a wealth of agronomic and crop improvement research that
stretches back to 1950 and often before. Factors such as time of sowing, plant
‡Corresponding author: p.dixit@cgiar.org
318 P.N.DIXIT et al.
population, weed control measures, fertilizer use, improved rainfall management
through mulching or contrasting tillage techniques and their interaction with
contrasting crop species and improved crop genotypes have been extensively studied
and reported. Typical of such research was that undertaken between 1965 and 1969
at the Grasslands Research Station, established in 1951 in Kitale, Kenya for, the new
hybrid maize (Zea mays) varieties that were being developed and released at that time
(Allan, 1972).
However one factor that constrained, and still constrains, such work is the length
of time that such studies could be continued. Typically, in the past, few of them have
exceeded four to five years and more recently evidence suggests that even shorter-
term studies are becoming the norm. A recent compilation of field-based research on
integrated soil fertility research in SSA (Bationo et al., 2007) presented the results of
105 pieces of integrated soil fertility related research. Of these, 57 were field-based
agronomic studies that covered a wide range of innovations including nitrogen (N)
and phosphorous (P) fertilizers, the use of farmyard manure and the inclusion of
leguminous crops and trees in rotations. Studies of interactions with contrasting water
conservation approaches such as the use of mulches and contrasting soil tillage were
also reported in several papers. Of these studies, thirty were for 1 year or less, eleven
for 2 years, ten for 3 years, three for 4 years, one for 5 years and one for 7 years. Whilst
most of these papers presented information on rainfall and rainfall ranges at the study
sites, none related the results obtained to the prevailing weather conditions. The results
of one long-term study (14 years) undertaken in Kenya (Kihanda et al., 2007) was an
exception in that an attempt was made to explain season-to-season variability in crop
responses to both manure amounts and to total seasonal rainfall through regression
analysis.
This begs the more general question as to what extent the rainfall seasons over
which such investigations were undertaken are able to ‘sample’ the natural season-to-
season and within season variability of rainfall, that is so evident in SSA, especially
in the drier areas. This variability is illustrated in Figure 1 which presents the long-
term (>30 years) mean seasonal rainfall totals from a range of locations in Eastern
and Southern Africa and their coefficients of variation (CV). It can be seen that the
inherent variability in seasonal rainfall totals increases disproportionately as one moves
from wetter locations to the semi-arid tropical (SAT) regions that receive between
250 and 600 mm of seasonal rainfall (Cooper et al., 2008). Whilst seasonal rainfall
totals themselves exhibit high variability, rainfall totals over shorter periods, perhaps
corresponding to moisture sensitive stages during crop growth such as germination
and flowering exhibit even greater variability.
The magnitude of such seasonal rainfall variability can be further illustrated
(Figure 2) by the probability distribution of the long-term seasonal rainfall data from
Bulawayo, Zimbabwe (1952–2007) where seasonal rainfall totals have a mean value
of 604 mm and a CV of 28%, with totals ranging from 179 to 1029 mm.
Since rainfall, stored in the soil profile, is the only source of water available for
crop growth and yield development, such magnitudes of variability of water supply
are likely to have profound impacts, not only on crop yield, but also on the potential
Climate risk assessment of maize production in Kenya 319
Figure 1. Means of seasonal rainfall totals (mm) and their coefficient of variation (%) for locations in Eastern and
Southern Africa (Cooper et al., 2008).
beneficial effects and rates of return that farmers may expect to receive from the
adoption of innovative crop, soil and water management practices and indeed for the
correct choice of crop variety or species. Not surprisingly, such effects have been noted
by researchers in the past. For example, Jones (1987) found that the optimum plant
population of sorghum (Sorghum bicolor) in the SAT of Botswana ranged from 25 000
to 69 000 plants ha−1over a rainfall total range of 200–700 mm in the seasons of
the study, and Smaling et al. (1992), based on 70 trials over four years in contrasting
agro-ecological zones in Kenya, noted that, in three years out of the four, farmers
could achieve rates of return of maize yield responses to fertilizer input that ranged
from 1.5 to 4.5, again depending on rainfall amounts. In seasons of very low rainfall,
rates of return fell below 1.
However as noted above, field-based investigations are hardly ever of sufficient
length to establish robust relationships between rainfall amounts and its distribution
and the resultant crop responses to contrasting management practices.
Given the (i) large variations of seasonal rainfall, (ii) the impact that rainfall amounts
and distribution will have on the results obtained through agronomic research and
(iii) the questionable ability of four to five seasons of research to capture the full
extent of the longer-term rainfall variability, it is important that approaches to more
detailed climate-induced risk analyses to accompany field-based research should be
evaluated. Without such risk analyses, farmers cannot be properly informed about
the full nature of such risk and the extent to which modifications to crop, soil
and water management recommendations may be made to mitigate the extent
320 P.N.DIXIT et al.
Figure 2. The probability of exceeding any given seasonal rainfall total (mm) at Bulawayo, Zimbabwe (1952–2007).
of such risk. Given the risk averse nature of small-scale farmers in SSA, this is
important.
One way to address this is through the use of crop growth simulation models
such as the Agricultural Production Systems Simulator (APSIM), which is a crop
simulation model which simulates the dynamics of crop growth, soil water, N and
soil carbon in a farming system (McCown et al., 1996). It operates on daily time
steps and when driven by long-term (>30 years) daily weather data, can be used to
predict the impact of seasonally variable rainfall, both amounts and distribution, on
the climate-induced risk associated with a range of crop, water and soil management
strategies. APSIM can simulate the impacts of such contrasting management options
on a range of crops amongst which maize, sorghum, pearl millet (Pennisetum americanum),
chickpea (Cicer arietinum), pigeon pea (Cajanus cajan), soyabean (Glycine max), groundnut
(Arachis hypogea) and sunflower (Helianthus annuus) are likely to be of most interest in
SSA. When properly calibrated for these crops, APSIM can provide an accurate
simulation of actual crop yields across a range of soil types and seasons (e.g. Dimes,
2005).
APSIM has been used in the tropics of sub-Saharan Africa by various researchers to
model fertilizer responses (Shamudzarira and Robertson, 2002), interactions between
previous leguminous crop and maize responses to N (Robertson et al., 2005) and
crop–weed interactions (Chikowo et al., 2008; Grenz et al., 2006).
However, often the existence, availability, access to, or required cost of high-
quality, long-term daily weather data for locations of interest can present very serious
constraints to the use of such models in SSA. Structural reform policies in the 1980s
encouraged national meteorological services to treat long-term historical weather data
Climate risk assessment of maize production in Kenya 321
as a source of revenue rather than a public good, and the reporting from an already
inadequate number of stations is declining (Washington et al., 2006, see also Hansen
et al., 2011).
In such instances, the use of spatial weather generators such as MarkSim can help
overcome this constraint. MarkSim is a spatially explicit daily weather generator that
was released in 2002 and is a weather generator which uses a third order Markov
chain process to generate daily weather parameters. It is specifically developed to
generate weather data for tropical regions. The climate surfaces that are produced use
data from 10 000 stations in Latin America and 7000 from Africa. MarkSim relies
on climatic data surfaces interpolated from weather stations and generates synthetic
rainfall records that are statistically similar to long-term patterns on a grid basis of
18 km ×18 km (Jones and Thornton, 2000).
Given the need for information on climate-induced risk and the availability of well-
tested crop growth simulation models and spatial weather generators, the objectives
of this study are:
1. To demonstrate the value of such tools in climate risk assessment to compliment
field-based research.
It is often felt that climate-induced risk is only likely to be of serious consequence
in more arid environments where rainfall amounts are low and highly variable
(Figure 1), so a secondary objective of this study was:
2. To assess the value of the outputs of such analyses in the context of a high
potential maize production area (in this case, Kitale, Kenya) where intuitively
climate-induced risk might be assumed to be unimportant.
The outputs of this work are related to the recommendations that were produced by
Allan (1972) for new long-duration maize hybrids (180 days to maturity) in Kitale
with special reference to recommendations concerning plant population (44 000 plant
ha−1), weed control (weeding at five weeks post emergence) and N fertilizer use
(30 kg N ha−1at sowing plus120 kg N ha−1at five weeks post emergence). Whilst the
research was undertaken by Allan more than 40 years ago, the recommendations that
resulted from this work are still being widely followed today in the high potential maize
production zones of Kenya. This climate-induced risk analysis thus remains relevant.
MATERIALS AND METHODS
Location
The study location, Kitale (1◦1N, 35◦0E, altitude 1890 m asl) is situated in the
Trans Nzoia district of Western Kenya. The region is considered a high potential
maize growing area and receives an average annual rainfall (1951–1986) of 1259 mm,
distributed in a uni-modal rainfall pattern largely between March and October.
Long-duration maize hybrids are widely grown. The average annual maximum and
minimum temperatures are 25.4 ◦C and 11.5 ◦C, respectively (FURP, 1987). The
322 P.N.DIXIT et al.
topography is largely gently undulating and the dominant soils are deep well-drained
sandy clay loams (rhodic Ferralsols).
Weather data generation using MarkSim
MarkSim has the option to either (a) generate weather data either using the location
co-ordinates and elevation as input to estimate weather parameters from interpolated
surfaces that have been incorporated in the MarkSim software or (b) by using long-
term (20–25 years) averages based on daily weather data of the location of interest
(Jones and Thornton, 2000). In this study the long-term maximum and minimum
temperatures and rainfall averages (FURP, 1987) were used to generate 50 years of
daily weather data (maximum and minimum temperature, rainfall and solar radiation)
for Kitale. The generated weather data were ready to use as an input into APSIM.
Available weather data
Daily weather parameters have been recorded at Kitale continuously since 1936
for the township and since 1951 for the Grasslands Research Station. However,
currently the cost of access to this daily data is prohibitive. Free access to such data
is possible through the National Oceanic and Atmospheric Administration (NOAA),
USA (ftp://ftp.ncdc.noaa.gov/pub/data/globalsod); however, records held there for
Kitale only date back to 1975 and are of poor quality with many missing values. Of
the 33 years held on the website, only 16 years were of sufficient quality (<four days
missing for the April to September growing season) to be of use in assessing how well
MarkSim simulated rainfall amounts and distribution patterns at Kitale.
Calibration of APSIM
The calibration was based on that already existing in APSIM for Hybrid 614 and
modified for Hybrid 6302, a very similar hybrid with regard to maturity type and
yield potential. Crop growth data were abstracted from Cooper (1979) together with
the base temperature calculated in that paper of 9 ◦C. Thermal time requirements
for the development stages (i) emergence to end of juvenile phase, (ii) end of juvenile
phase to flag leaf initiation and (iii) flag leaf initiation to 50% tasselling were adjusted
to obtain the best fit between observed and simulated crop growth and yield.
Other inputs required for APSIM include the soil water retention parameters, e.g.
water content at saturation, field capacity and permanent wilting point along with the
soil bulk density. Soil organic carbon values are also required. This information was
available in detail to a depth of 210 cm (Cooper and Law, 1977). A total of 239 mm
of water was available up to the depth of 205 cm. The maximum available water to
the crop was set at 40% of the total available water, i.e. 96 mm. The initial mineral N
of the soil profile was set at 15 kg ha−1NO3and 10 kg ha−1NH4. For the purposes of
this study, P was assumed to be non-limiting in all simulations although Allan (1972)
reported frequent responses to P in the soils of the Trans Nzoia district and indeed
recommended a dressing of 500 kg ha−1of single super phosphate at sowing.
Climate risk assessment of maize production in Kenya 323
The sowing rule was set so that sowing took place when there was an accumulation
of 50 mm of rainfall within a five consecutive days period. The sowing window was
kept between 15 March and 31 May to ensure early sowing of the crop as near to the
onset of the rainy season as recommended by Allan (1972).
With this set-up, the growth and yield of maize H6302 was simulated for the year
1977 which was one of the years when observed daily weather data of sufficient quality
were available. The crop was planted on April 28 with a basal dressing of 30 kg N
ha−1and 110 kg N ha−1at 35 days post emergence (d.p.e) and the crop was kept
weed free according to the management used by Cooper (1979). The simulated dry
matter accumulation, leaf area and grain yield were compared with those observed
by Cooper (1979) and are presented in Table 1.
Treatments simulated
We examined a factorial combination of eight levels of N application and three
levels of weed control. This provided a factorial 24 treatments, which is probably
more than would be advocated in field experimentation but is one advantage of the
use of a crop growth simulation model where specific treatments can be investigated
at this level of detail without the corresponding costs associated with field work. Apart
from the control with no N application, N was applied as a basal dressing of 30 kg
ha−1to all treatments, with additional top dressings of 0, 30, 60, 90, 120, 150 and 180
kg N ha−1providing the eight levels of 0, 30, 60, 90, 120, 150, 180 and 210 kg N ha−1.
The three weed control treatments were (i) ‘no weeding’ apart from that achieved
through seedbed preparation and planting operations, (ii) ‘weed free’ which simulated
a pre-emergence spray of the herbicide atrazine (4 kg ha−1active ingredient) and (iii)
weeding at five weeks post emergence (w.p.e). Weeds were allowed to regrow between
5 w.p.e and maturity. In the weed treatments (i) and (iii) weed population was set at
24 plants m−2and, similar to other weed simulation studies at Kitale (Chikowo et al.,
2008), the ‘summer grass’ weed option of APSIM was chosen.
As a second study, we examined the effect of plant population density under a
single management combination of current recommendations for N application (30+
120 kg N ha−1) and weed control (at 5 w.p.e). Maize yield responses to plant populations
of 10, 20, 30, 40, 50 and 60 thousand plants ha−1were examined by keeping the
between row spacing at 75 cm as currently recommended and varying the within row
spacing to achieve the desired population.
Partial budget analyses
Rates of return to both N application and to weed control at 5 w.p.e were
investigated. Using an exchange rate of 75 Kenya Shillings to 1 US$ and current
fertilizer and maize prices, 1 kg of N applied was priced at 2.86 US $ kg N−1and 1 kg of
maize grain was valued at $ 0.252 kg−1. Labour for fertilizer application was not costed.
Based on a requirement of 20 person days ha−1for weeding (Jama et al., 1998) and
the current government minimum daily minimum wage for Western Kenya of $3.5
day−1, the cost of weeding at 5 w.p.e was set at $70 ha−1.
324 P.N.DIXIT et al.
Table 1. Observed (Cooper, 1979) and simulated (APSIM) growth of maize H6302 at Kitale, 1977.
Days post 75% emergence
59 98 180
Total dry matter (kg ha−1) 12th visible leaf 50% tasselling Crop maturity
Observed 3832 12 394 19 740
Simulated 3169 11 036 20 377
Leaf area index
Observed 2.06 2.52 –
Simulated 1.51 2.36 –
Grain yield (kg ha−1)
Observed – – 8 800
Simulated – – 8 640
Harvest index
Observed – – 0.445
Simulated – – 0.424
RESULTS
Calibration of APSIM
Given the similarity of H614 and H6302 in terms of morphology, maturity length
and yield potential together with the fact that the abstracted data (Cooper, 1979)
were used to adjust thermal times to obtain the best fit with observed data, the good
agreement between observed and simulated data (Table 1) was to be expected, but
nevertheless is encouraging.
It was noted from this and subsequent simulations that, as set up in this study,
APSIM tended to underestimate the rate of early growth of H6302. This is reflected
in total dry matter production and leaf area development at 59 d.p.e. (Table1). Cooper
and Law (1977) reported dry matter production at 5 w.p.e for H613C for 16 trials over
the period 1972–1976 which ranged from 264 to 885 kg ha−1. In this calibration, dry
matter at 5 w.p.e was 325 kg ha−1. However, by 50% tassel emergence and at maturity,
the agreement between observed and simulated data was good. In spite of the wealth
of maize growth and yield data available from Kitale between 1972 and 1977 (i.e.
Cooper and Law, 1978b), a more rigorous evaluation of APSIM as calibrated for this
study with independent data was not possible due to lack of access to long-term daily
weather data for those years. Nevertheless, subsequent simulations (discussed later)
closely reflected maize grain yield responses to management reported in the literature.
Comparisons of (i) generated, (ii) observed and (iii) long term mean weather data
We undertook a simple evaluation to ascertain how well the 50 seasons weather
data generated by MarkSim represented observed weather data recorded at Kitale,
both the long-term averages (FURP, 1987) and the 16 years of daily weather data that
we had at hand. Monthly (April–September) mean Tmax, Tmin and solar radiation
values and rainfall totals are compared in Table 2 for three data sources.
We first compared long-term average values (FURP, 1987), with average values of
the data generated by MarkSim.
Climate risk assessment of maize production in Kenya 325
Table 2. MarkSim generated, observed (16 years) and long term average weather data for Kitale, Kenya.
Variable Data source April May June July Aug Sept
Seasonal means
and totals
Tma x ( ◦C) MarkSim 26.0 25.0 24.1 23.4 23.3 24.0 24.3
Long-term mean (FURP) 25.8 24.8 24.1 23.3 23.7 24.8 24.4
16 years observed 26.0 25.2 24.2 23.7 24.3 25.3 24.7
Tmi n ( ◦C) MarkSim 13.0 12.8 11.5 11.6 11.0 10.9 11.8
Long-term mean (FURP) 12.9 12.6 11.7 11.6 11.3 11.0 11.9
16 years observed 13.2 13.1 12.4 11.6 11.5 11.1 12.1
Radiation (MJ m−2d−1) MarkSim 19.0 17.1 16.9 16.5 16.8 18.9 17.5
Long-term mean (FURP) 20.5 19.9 19.5 18.2 19.1 20.9 19.7
16 years observed n/a n/a n/a n/a n/a n/a –
Total rainfall (mm) MarkSim 171 207 104 135 132 101 850
Long-term mean (FURP) 190 198 101 123 151 103 866
16 years observed 189 153 107 137 150 74 810
FURP: Fertilizer Use Recommendations Project.
The monthly mean values (Tmax and Tmin) and monthly total rainfalls as generated
by MarkSim show good agreement with the long-term values (FURP, 1987). However,
the radiation values generated by MarkSim are consistently lower across months
(approx. 10%) compared with those observed for the long-term average. Dry matter
production rates will be affected by levels of radiation. For example, increasing the
MarkSim generated radiation value used in the simulations by 10% resulted in a
12.5% increase in dry matter production at 35 d.p.e. However, in the simulation
results reported in the rest of this paper, the radiation values were kept as those
generated by MarkSim.
We next compared the means of the 16 years of available data with the long-term
averages (FURP, 1987). The monthly mean Tmax and Tmin and monthly total rainfall
values derived from the 16 years of available observed weather data also compared
favourably with the reported long-term (35 year) means (FURP, 1987). With the caveat
that this is a comparison of 16 years with the 35-year averages given by FURP, this
allows a more detailed comparison rainfall distribution patterns between MarkSim
and the 16 years’ observed data.
First we looked at the probability distribution of seasonal rainfall totals (Figure 3).
The MarkSim generated dataset contained four seasons that were clearly drier than
any found in the observed data and one that was wetter. We would note here that the
longer the simulated record, the greater the frequency of such ‘extremes’ is likely to
be. However, the distribution of the total rainfalls of the remainder (45 seasons) of the
generated dataset corresponded well with observed values.
We then looked at rainfall distribution patterns within the season through a simple
comparison of the frequency of rainfall events of different sizes and the frequency of
dry spells of different lengths. Differences between mean values for both parameters
were tested using a two-tailed t-test, having first tested for equal or unequal variance
within the two sets of data (Table 3).
326 P.N.DIXIT et al.
Table 3. Comparison of the seasonal (April–September) lengths of dry spells and number of rainfall events of
different size from 50 years simulated data (MarkSim) and the observed rainfall data (16 years), Kitale, Kenya.
Average number of rain events per season
Size of rainfall event (mm) >0–<55–<15 15 – <30 >30 Total events
MarkSim 47.9 29.7 13.7 5.0 96.3
Observed 57.4 32.8 9.3 3.7 103.2
Significant difference between means p<0.01 p<0.01 p<0.05 p<0.05 n.s.
Average number of dry spells per season
Dry spell length (d) 3–4 5–6 7–8 9–10 >10
MarkSim 4.6 2.4 1.4 0.7 1.4
Observed 5.9 1.5 0.3 0.5 0.3
Significant difference between means n.s. p <0.05 p<0.001 n.s. p <0.001
Figure 3. The probability distribution of seasonal rainfall totals (mm) from 16 years observed data and from 50 years
simulated data (MarkSim) for Kitale, Kenya.
The MarkSim dataset significantly underestimates the number of days per season
with >0to<5and5to<15 mm of rainfall, and overestimates the number of days
with the larger storm sizes of 15 to <30 and >30 mm. The combined results of this
latter observation is (i) that there is no significant difference in the total number of
rainfall events per season and (ii) MarkSim has generated rainfall totals that are on
average higher than those found in the 16 years of observed data (see Table 2 and
Figure 3).
Compared with the observed data, MarkSim significantly overestimates dry spells
of 5–6, 7–8 and >10 days duration. Given that a greater frequency of longer dry spells
is generated by MarkSim, thus providing fewer opportunities for the shorter dry spells,
it is not surprising that MarkSim slightly underestimates (though not significantly) the
frequency of relatively short dry spells of 3–4 days duration. We further illustrate the
Climate risk assessment of maize production in Kenya 327
Figure 4. Mean longest dry spell per month for Observed (clear bars) and MarkSim (shaded bars) weather data with
the dry spell ranges for 20 and 80% probability exceedance indicated, for Kitale, Kenya
overestimation of longer dry spells by MarkSim through a comparison of the longest
dry spells on a monthly basis within the MarkSim and Observed datasets (Figure 4).
In this figure we show both the ‘average longest dry spell per month’, and the range
of dry spell values that lie between the 20 and 80% probability of exceedance for each
month. MarkSim clearly generates longer dry spells than in the observed data both
in terms of means and the range of values generated in all months, as is particularly
evident in July and August. We discuss the implications of these long dry spells later in
relation to our examination of weather variables that are contributing to risk at Kitale.
An overview of mean maize yield responses to treatments simulated
The mean of the 50 years of simulated maize responses to N-application and
weed control were first examined to assess to what extent they corresponded to those
reported in previous research (Figure 5). Mean N-responses, both in the weed-free
treatment and the weeding at 5 w.p.e increased up to an application of 210 kg N ha−1,
but the increases in yield beyond the application of 150 kg N ha−1were small.
The mean rates of returns to N-application were calculated for the 30 N ha−1
increments in the ‘weeding at 5 w.p.e’ treatment since this weed control measure is
more likely to be used by small-scale farmers than achieving a weed-free condition
through the use of a pre-emergence herbicide. It can be seen that beyond a rate of
150, rates of return fell below a value of 2 which would be unlikely to be of interest to
risk-averse farmers. This suggests that a recommendation of a split dressing of 30 +
120 kg N ha−1would be ‘optimum’ and is similar to the 140 kg N ha−1recommended
by Allan (1972).
328 P.N.DIXIT et al.
Figure 5. Mean maize yield responses from 50 years of simulations (APSIM) to N application and weed control at
Kitale, Kenya.
Controlling weeds through a combination of seed-bed preparation and weeding at
5 w.p.e resulted in a slight depression of the N-response curve below the ‘weed-free’
scenario, which would be expected. However the yield depression was comparatively
small confirming the importance of weeding at 5 w.p.e as reported by Allan (1972).
It is interesting to note that the yield depression due to the low levels of weed growth
that occurred during the first five weeks, and again between five weeks and maturity,
decreased with increasing rates of N-application, suggesting that in such high rainfall
environments under this weeding regime, weed competition for N is more important
than for water.
The ‘no weed control’ treatment resulted in substantial yield depression and greatly
reduced responses to N-application. This again closely reflects the findings reported
by Allan (1972).
We also examined the simulated maize responses to increasing plant populations
(Figure 6). Maize yields increased substantially up to a population of 40 000 plants
ha−1, but thereafter responses to higher plant populations were negligible. This is very
much in line with the current recommendation of planting 180 day hybrid varieties
at 44 000 plants ha−1in the Kitale environment (Allan, 1972).
Probability distribution of maize yield responses to N application
The probability distributions of maize yields responses to the different rates of
N-fertilizer application were examined for the 50 seasons of simulations (Figure 7).
The probability distribution curves for responses to N for fertilizer increments up to
150 kg N ha−1run roughly parallel to each other, indicating that responses to N-
fertilizer do not vary a great deal between the lower yield potential seasons and the
Climate risk assessment of maize production in Kenya 329
Figure 6. Mean maize yield responses from 50 years of simulations (APSIM) to plant population at Kitale, Kenya.
Figure 7. Probability distribution of simulated (APSIM) maize yields at different fertiliser levels (kg N ha−1)with
weeding at 5 weeks post emergence at Kitale, Kenya.
higher potential seasons and that there was no interaction between the application of
N (at these levels) and the type of season.
For the higher levels of application (180 and 210 kg N ha−1), this picture changes.
In the lower potential seasons, i.e at the 90% probability of exceedance, there are no
additional responses to high rates of N application, but as the season’s yield potential
increases at the 5% probability of exceedance, additional responses to higher levels of
N application are observed. This is further illustrated in Table 4 where we examined
the range of yields, interpolated from Figure 6, that lay between the 95 and 5%
probability of exceedance for the different N application rates.
Clearly, the range of yields across different levels of N-application dwarf those found
within any rate of application emphasizing the importance of N availability in these
high rainfall areas. However, the ranges within a given level of N-applications are also
significant. Since the only model input factors to vary from season to season within any
rate of N-application are the weather variables associated with temperature, radiation
330 P.N.DIXIT et al.
Table 4. Simulated (APSIM) yield ranges of maize (kg ha−1) lying between the 95 and 5% probability
of exceedance at different levels of N-application at Kitale, Kenya.
Fertilizer rate (kg N ha−1) 0 30 60 90 120 150 180 210
Yield at 95% exceedance 420 1880 3670 4820 5770 6370 6400 6400
Yield response to each 30 kg N ha−1increment at 95%
exceedance
– 1460 1790 1150 948 600 30 0
Yield at 5% exceedance 1970 3620 5250 6450 7540 8680 9380 10 010
Yield response to each 30 kg N ha−1increment at 5%
exceedance
– 1650 1630 1200 1090 1140 700 630
Yield range between 95 and 5% exceedance within
each fertilizer rate
1550 1740 1580 1630 1770 2310 2980 3610
and rainfall, these must also be impacting on crop growth and yield formation. We
examined this in more detail later.
Cooper and Law (1978b) reported maize yields from a range of early planted and
‘weed-free’ trials conducted between 1972 and 1977 to which 140 kg N ha−1had
been added as the standard recommended rate. Over those years, maize yields ranged
from 7510 to 9120 kg ha−1. Reference to Figure 6 and Table 4 indicates the good
correspondence of the simulated maize yields (150 kg N ha−1applied) and those
observed by Cooper and Law.
Rates of return to N-application
In this analysis, we assumed that farmers had applied 30 kg N ha−1as a basal
dressing as recommended and then examined the rates of return ($ per $ invested)
that a farmer might expect to achieve from subsequent top dressings at 5 w.p.e of 30,
60, 90, 120, 150 and 180 kg N ha−1(Figure 8).
The rates of return achieved for the basal dressing of 30 kg N ha−1were consistently
high, exceeding a ratio of 4 in 95% of the seasons. This would be attractive, even for
risk-averse farmers and supports the current recommendation for such a basal dressing.
Top dressing with a further 30 kg N ha−1seems equally attractive, again exceeding a
ratio of nearly 4 in 90% of the seasons. For higher rates of top dressing, the rates of
return fell. Whether or not a farmer would choose such higher rates of top dressing
would depend on the rate of return required, how many years out of ten would be
needed to achieve that rate and the farmer’s assessment of the state of the season
and crop at 5 w.p.e. This is illustrated further in Table 5 for values interpolated from
Figure 8.
The results provided in Table 5 are illustrative of the value of this type of analyses in
that they can provide practical guidelines to farmers with contrasting degrees of risk
aversion. However, prices of fertilizer and maize can fluctuate greatly from season to
season, so sensitivity analyses examining the impact of such price fluctuations could
further enhance the value of such information. An example, assuming a 20% increase
in the cost of N-fertilizer and a 20% decrease in the value of maize is given (Table 5)
to illustrate this.
Climate risk assessment of maize production in Kenya 331
Table 5. Rates of N top dressing on maize (kg N ha−1) required to achieve contrasting rates of return with different
levels of probability of achieving required rate of return, Kitale, Kenya.
Minimum rate of return required (N =
$2.86 kg−1:Maize=$0.252 kg−1)
Minimum rate of return required (N =
$3.43 kg−1: Maize =$0.202 kg−1)
Years out of 10 rate
of return is required 1 2 3 4 5 1 2 3 4 5
9 180 150 120 0 0 180 120 0 0 0
7 180 180 150 60 0 180 150 0 0 0
5 180 180 150 90 30 180 150 30 0 0
Figure 8. Probability distribution of rates of return ($ per $ invested) for different fertilizer levels (kg N ha−1)with
weeding at 5 weeks post emergence at Kitale, Kenya.
Rates of return to hand weeding at 5 w.p.e.
The probabilities of exceedance of rates of return to hand weeding at 5 w.p.e at
different levels of N application are shown in Figure 9. Clearly, when N fertilizer is ap-
plied, weeding is both essential with regard to maintaining yield potential (see Figure 5)
and highly profitable in terms of rates of return to labour costs, ranging between 5
and 35 in 90% of seasons across all fertilizer levels. Only in the absence of N fertilizer
applications were they marginal in that in the 30% lower potential seasons, rates of
return fell below 1.
Probability of maize yield responses to plant population
Mean simulated maize yield responses to increasing plant population (Figure 6)
closely reflect those found in field studies and current recommendations. They are
illustrated as probabilities in Figure 10 for the current recommendation of 150 kg N
ha−1and with weeding at 5 w.p.e. The range of yields simulated within the low plant
population of 10 000 plants ha−1is small compared with higher plant populations,
indicating that low plant population is the main limiting factor rather than weather
variables. However as plant populations increase and become less of a constraint to
yield potential, weather variables play an increasing role and as a result the yield range
increases.
332 P.N.DIXIT et al.
Figure 9. Probability distribution of rate of returns ($ per $ invested) to weeding at 5 weeks post emergence,
Kitale, Kenya.
Figure 10. Probability distribution of simulated (APSIM) maize yields at different plant populations (000’s ha−1)with
150 kg N ha−1and weeding at 5 weeks post emergence at Kitale, Kenya.
Rates of return to planting extra seed to achieve increasing plant population are
very high indeed and are not examined in detail. The current hybrid maize seed price
from Kenya Seed Company is $37 per 25 kg bag. Assuming a 1000-grain weight of
420 g (Cooper and Law, 1977), an increase of 10 000 plants ha−1requires 4.2 kg
additional seed costing $6.2. With a maize value of $ 0.252 kg−1, only an additional
25 kg ha−1of yield is required to cover those costs. Even allowing for the extra labour
required for planting more seeds per hectare, there is no risk involved in planting at the
recommended population of 44 000 plants ha−1in such a high potential environment.
The influence of weather factors on maize growth and yield
APSIM simulates the main effects, and their interactions, of air temperatures,
radiation levels and available water in discrete soil layers on the rate of maize
development, dry matter accumulation and final grain yield in daily time steps and
through a complex series of interrelated process sub-models. The purpose of our
simple analysis is not to try to duplicate the level of sophistication of such procedures,
Climate risk assessment of maize production in Kenya 333
but to approximate the model output by simple linear relationships in order to explore
the relative importance of weather variables in contributing to climate risk at Kitale.
To undertake this analysis, we simulated maize growth using the 50 years of weather
data generated by MarkSim, but set the APSIM simulations under ‘nutrient non-
limiting’ and weed-free conditions to ensure that the influence of weather variables
were separated from other effects. We examined the crop growth rates (CGRs) in
four distinct phases of crop growth namely: (i) 0–40 d.p.e., corresponding to early
exponential growth, (ii) 40–80 days d.p.e, corresponding to the linear phase of
vegetative growth, (iii) 80–120 days d.p.e, corresponding to the period of flowering
and seed set and (iv) 120–160 days d.p.e, corresponding to the linear phase of grain
filling. For each time period, the analysis related the CGR simulated during the period
for each of the 50 years to mean air temperatures, mean radiation levels and to the
mean water stress index (WSI), an APSIM output which ranges between 0 and 1, and
is the index of actual water uptake from the soil, summed over all soil layers, relative
to potential biomass growth demand for water on a day. Thus a value of 1 is attained
when there is no water stress.
In addition, the biomass at the start of the period being examined was included as a
variable since that reflects the ‘starting potential’ for subsequent biomass production
in the following period. Thus for any given time period:
CGR =(f1)×(B)+(f
2)×(T) + (f3)×(R)+(f
4)×(WSI)
where:
CGR =crop growth rate in kg ha−1day−1
B=biomass at start of the period in kg ha−1
T=mean air temperature for the period in ◦C
R=mean radiation during the period in MJ m−2day−1
WSI =mean water stress index for the period.
The highest and lowest mean values of the variables under consideration are shown
for the four crop growth stages in Table 6 to indicate the ranges encountered in this
study and regression equations obtained are given in Table 7.
In the 0–40 d.p.e period, the mean crop growth rates for the 50 years were positively
influenced by a favourable water supply (i.e. a high WSI value), high radiation levels
and warm temperatures. This reflects what has been shown in field studies (Cooper
and Law, 1977). During this period, rapid production of leaf area is essential and will
be driven by the effect of temperature on (i) the leaf emergence rate and (ii) the rate of
photosynthesis. Clearly, high radiation levels will also promote high rates of dry matter
production. During this period, root growth is largely restricted to the 0–15cm soil
depth interval, a fact that APSIM builds into the calculation of the WSI factor. Thus
relatively short dry spells leading to soil surface drying will have pronounced negative
effects on crop growth. As the crop started to enter its linear phase of vegetative growth
(40–80 d.p.e), the biomass at the start of the growth period became the major factor
determining subsequent potential crop growth rates, but this was modified by the levels
334 P.N.DIXIT et al.
Table 6. Range of values in the four growth periods of simulated (APSIM) maize growth
at Kitale, Kenya.
Crop growth period (d.p.e) 0–40 40–80 80–120 120–160
Crop growth rate (kg ha−1d−1) Highest 20.4 214 364 506
Lowest 2.3 88 201 268
Dry matter at start (kg ha−1) Highest 0 817 8566 14 590
Lowest 0 93 3534 8045
Mean air temperature (◦C) Highest 21.1 20.7 21.3 20.2
Lowest 15.9 15.5 14.2 15.5
Mean radiation (MJ m−2d−1) Highest 22.2 21.2 21.8 24.1
Lowest 13.2 12.8 12.0 13.8
Mean WSI Highest 1.0 1.00 1.00 1.00
Lowest 0.62 0.85 0.89 0.87
WSI: water stress index.
Table 7. Linear regression equations relating simulated crop growth rates (CGR) to
weather variables and biomass for four growth periods at Kitale, Kenya.
Time period (d.p.e) Multiple linear regression equation R2-value
0–40 CGR =−41.9 + 22.4WSI∗∗ +0.75R∗∗ + 0.90T∗0.42
40–80 CGR =34.4 + 0.11B∗∗ + 3.90R∗0.54
80–120 CGR =−138 + 0.031B∗∗ + 265WSI∗0.78
120–160 CGR =−338 + 0.026B∗∗ + 455WSI∗0.80
∗∗ p<0.01; ∗p<0.05.
WSI: water stress index.
of radiation that the crop received. In the subsequent periods (80–120 and 120–160
d.p.e), the biomass at the start of the period continued to be the dominant factor setting
the potential for crop growth rates during the period, but in both these periods, water
stress (i.e. low WSI values) was shown to limit growth. In other words, high crop growth
rates during early growth are important in setting the potential for subsequent growth
and yield development of maize at Kitale. This is consistent with field-based results
and is discussed later. We note here however, that whilst simple linear relationships
have identified key weather variables influencing crop growth rates, their limitation in
reflecting the complex interactions of temperature, radiation and moisture supply is in-
dicated by the unexplained variation which, in these simulations is still due to weather.
DISCUSSION
It is beyond the scope of this paper to undertake a detailed statistical comparison
between the weather data simulated by MarkSim, the long-term averages and the
16 years of daily data at hand, although such studies have been undertaken comparing
the performance of a range of available weather generators (e.g. Hartkamp et al.,
2003). For the purpose of this paper, we have endeavoured to show that the 50 years
of generated weather data was a sufficiently close approximation to the weather data
that we had at hand to allow its use as an input into APSIM. The monthly means
(temperature) and totals (rainfall) (Table 2) generated by MarkSim were in close
Climate risk assessment of maize production in Kenya 335
agreement with observed data, but the generated radiation levels were consistently
about 10% lower than the long-term averages. The average total number of rainy days
per season and the distribution of rainfall events of different sizes (Table 3) generated
by MarkSim were also in good agreement with those observed in daily data at hand.
The greatest discrepancy lay in the comparison of the frequency of dry spells (Table 3
and Figure 4) where it was observed that MarkSim generated many more dry spells of
greater than 10 days than existed in the observed data. The bulk of these long dry spells
occurred towards the end of the rainy season during late August / early September, but
others occurred during June and at the start of the rains in April. Although these were
not noted in the 16 years observed data, Cooper and Law (1978b) noted periods of up to
20 days with very low rainfall totals (<10mm) at Kitale at the start of the rains between
1973 and 1975. Overall, we feel that for the purposes of use in crop simulation models,
and if long-term daily weather data is not available, the MarkSim generated daily data
for Kitale was of sufficient quality, reflecting the conclusions of Hartkamp et al. (2003).
This is also borne out by maize growth and yield outputs generated by APSIM, which
closely reflected the results obtained from field-based studies discussed below.
It was fortunate that the calibration of Hybrid 614 already exists within APSIM, that
crop growth data were available from Cooper (1979) to adjust that calibration for the
very similar Hybrid 6302 and that the required recorded weather data was available
for the year in which the field study was undertaken. It was unfortunate, however, that
the calibration we achieved could not be more rigorously evaluated by comparing
simulated maize yields with those observed in years other than the calibration year for
which a wealth of crop growth data exist (Cooper and Law, 1978b). However, this was
a particular circumstance to this study. In practice, the added workload to collect the
soil, crop and weather data as part of an agronomic field study in order to calibrate
and rigorously validate ASPIM is not great, and should not prove a deterrent given
the added value that such research can provide in terms of climate risk assessment.
The simulated maize growth and yield and yield responses to the management
factors of weed control, planting population and N-fertilizer application closely reflect
results of agronomic and crop physiological studies reported from Kitale (Allan, 1972,
Cooper and Law, 1977; 1978a;b) and clearly added value to shorter-term field-based
studies in that they were able to identify to what extent climate-induced risk might
impact on crop management recommendations for such a high rainfall area. This
was particularly evident in the case of N-application where the rates of return where
shown to vary considerably both across and within any given rate of N-application
(Figure 8), depending on the yield potential of different seasons. For example, for the
currently recommended application of 150 kg N ha−1,theratesofreturnrangedfrom
1.6 to 4.7. Naturally, in drier environments such as the SAT, where moisture supply is
both lower and a great deal more variable, rates of return will also vary a great deal
more. For example, in the semi-arid tropics of Zimbabwe, they can range from −8
to +12 for rates of N-applications of between 17 and 52 kg N ha−1(Dimes, 2005).
Nevertheless, knowledge of such risk associated with fertilizer use is still of great value
in high rainfall areas as risk aversion will vary from farmer to farmer, depending on
their circumstances. Being able to tailor fertilizer recommendations more specifically
336 P.N.DIXIT et al.
to individual farmer’s risk aversion whilst also taking into account fertilizer and maize
price fluctuations should clearly be of value (Table 5). This should be especially useful
with regard to the top dressing of N at 5 w.p.e as by that stage farmers will have a good
feel for how the season has developed and the state of their crops. Such knowledge is
likely to affect the level of risk that an individual would be prepared to accept in any
given season.
Our simple investigation into which weather parameters were playing a role in
causing ‘weather-induced risk’ also reflect the results of studies undertaken at Kitale
between 1972 and 1977. Cooper and Law (1977; 1978a) reported results which
concentrated on weather variables affecting the early growth of maize between 0 and
35 d.p.e. From results obtained from 12 trials undertaken in 1973, 1974 and 1975,
they established the relationship:
W5=6.27T∗∗ −0.33N∗∗ −93.6(R2=0.88)
where W5=theweightoftheplantatfiveweeks(gpl
−1), T =the mean air temperature
(◦C) and N =the number of days the 0–15 cm soil horizon was at or below wilting
point and ∗∗ p<0.01. Those results reflect the results obtained in this study where
moisture stress and air temperatures were also found to be important in determining
early dry matter production rates. Unlike Cooper and Law, this study also suggested
that radiation was a limiting factor during early growth. However, during the early
growth periods in the Cooper and Law years of study, mean radiation levels for the
early growth period ranged from 18.6 to 23.1 MJ m−2d−1, whilst in this simulation
study, they fell much lower with mean values ranging from 13.2 to 22.2 MJ m−2d−1.
(Table 6).
Cooper and Law (1977) also concluded that at Kitale, the rate of early growth was
very important in setting the yield potential of the crop and, for the years under study,
were able to establish a linear relationship between the weight of the plant at five
weeks and the final grain yield. However, during those years, concurrent moisture
studies (Cooper and Law, 1978b) indicated that no prolonged dry spells of >10 days
occurred and there was no moisture stress during vegetative growth and grain filling.
We were unable to establish a relationship of dry matter produced by 40 d.p.e and final
grain yield. However in these simulations our analyses showed that both low radiation
levels (40–80 d.p.e) and moisture stress (80–160 d.p.e) had negative impacts on the yield
potential set during early growth (Table 7). However, this study did show that the initial
biomass at the start of each growth period from 40 d.p.e onwards was the most signif-
icant factor determining subsequent crop growth rates (Table 7), confirming that the
potential set during early growth had a continuing role in determining final grain yield.
CONCLUSIONS
Whilst weather-induced risk in this high potential maize growing environment is clearly
not as great as that likely to be experienced in more arid locations and was shown not to
be important in influencing recommendations for sowing rates and weed control, it is
Climate risk assessment of maize production in Kenya 337
still a factor that clearly influences the rates of return and hence risk associated with N
fertilizer use. As such the type of climate risk analyses that we have demonstrated in this
study has value. This is especially true since (i) such high potential environments tend
to be the ‘bread baskets’ that ensure national food security and (ii) that widespread
N deficiency is recognized as a major constraint to crop production in SSA. Crop
growth simulation models such as APSIM, when driven by sufficient years of long-
term weather data, have an important role in quantifying such climate-induced risk.
Availability of long-term daily weather data is likely to remain a serious constraint and
our study has shown that weather generators such as MarkSim can play an invaluable
role in this respect. However, we would caution against using such weather generators
without some form of comparison of their output with, at the very least, long-term
monthly mean weather data from a nearby recording station. Finally, in an era when
increasing attention is being given to climate-induced risk and rainfed agriculture, and
possible changes in the nature of that risk as a result of climate change, we would make
a plea for greater collaboration between agricultural and meteorological services in
Africa and for greater ease of access to historical weather records.
Acknowledgements. The authors are grateful to the African Development Bank who
provided funds through the Association for Strengthening Agricultural Research in
East and Central Africa (ASARECA) to support the project ‘Managing Uncertainty:
Innovation systems for coping with climate variability and change’ which were used,
in part, to support this study. They are also grateful for the very valuable comments
and advice of two reviewers which greatly improved the manuscript.
REFERENCES
Allan, A. Y. (1972). T he influence of agronomic factors on maize yields in Western Kenya with special reference to time of plantings.
PhD Thesis, University of East Africa, Uganda.
Bationo, A., Waswa, B., Kihara, J. and Kimetu, J. (eds.) (2007). Advances in Integrated Soil Fertility Management in sub-Saharan
Africa: Challenges and Opportunities. Dordrecht: Springer.
Chikowo, R., Corbeels, M., Tittonell, P., Vanlauwe, B., Whitbread, A. and Giller, K. E. (2008). Aggregating field-scale
knowledge into farm-scale models of African smallholder systems: Summary functions to simulate crop production
using APSIM. Agricultural Systems 97: 151–166.
Cooper, P. J. M. and Law, R. (1977). Soil temperature and its association with maize yields variations in the Highlands
of Kenya. Journal of Agricultural Science, Cambridge 89: 355–363.
Cooper, P. J. M. and Law, R. (1978a). Enhanced soil temperature during very early growth, and its association with
maize development and yield in Highlands of Kenya. Journal of Agricultural Science, Cambridge 89: 569–577.
Cooper, P. J. M. and Law, R. (1978b). Environmental and physiological studies of maize. Final Report-Part 3, Volume 1.
Kenya Ministry of Agriculture and UK Ministry of Overseas Development, Maize Agronomy Research Project
(R2536 and R2989).
Cooper, P. J. M. (1979). The association between altitude, environmental variables, maize growth and yield in Kenya.
Journal of Agricultural Science, Cambridge: 93: 635–649.
Cooper, P. J. M., Dimes, J., Rao, K. P. C., Shapiro, B., Shiferaw, B. and Twomlow, S. (2008). Coping better with
current climatic variability in the rain-fed farming systems of sub-Saharan Africa: An essential first step in adapting
to future climate change? Agriculture, Ecosystems and Environment 126: 24–35.
Dimes, J. P. (2005). Application of APSIM to evaluate crop improvementtechnologies for enhanced water use efficiency
in Zimbabwe’s SAT. In Management for Improved Water Use Efficiency in the Dry Areas of Africa and West Asia: Proceedings
of a Workshop Organized by the Optimizing Soils Water Use (OSWU) Consortium. April 2002, Ankara, Turkey. 203–214.
FURP. (1987). Fertilizer Use Recommendations Project (Phase I). Final Report Annex III. Description of the First priority
338 P.N.DIXIT et al.
Sites in the Various Districts. Volume 10. Trans Nzoia District. Ministry of Agriculture and National Agricultural
Laboratories Nairobi, June 1987.
Grenz, J. H., Manschadi, A. M., Voil, P., Meinke, H. and Sauerborn, J. (2006). Simulating crop-parasitic weed
interactions using APSIM: model evaluation and application. European Journal of Agronomy 24: 257–226.
Hansen, J. W., Mason, S. J., Sun, L. and Tall, A. (2011). Review of seasonal climate forecasting for agriculture in
sub-Saharan Africa. Experimental Agriculture 47: 205–240.
Hartkamp, A. D., White, J. W. and Hoogenboom, G. (2003). Comparison of three weather generators for crop
modeling: a case study for subtropical environments Agricultural Systems 76: 539–560.
Jama, B., Buresh, R. J. and Place, F. M. (1998). Sesbania tree fallows on phosphorus-deficient sites: maize yield and
financial benefit. Agronony Journal 90: 717–726.
Jones, M. J. (1987). Plant population, rainfall and sorghum production in Botswana. 1. Results of experiment station
trials. Experimental Agriculture 23:335–347.
Jones, P. G. and Thornton, P. K. (2000). MarkSim: Software to generate daily weather data for Latin America and
Africa. Agronomy Journal 92: 445–453.
Kihanda, F. M., Warren, G. P. and Micheni, A. N. (2007). Effects of manures application on crop yield and soil
chemical properties in a long-term field trial in semi-arid Kenya. In Advances in Integrated Soil Fertility Management
in sub-Saharan Africa: Challenges and Opportunities, 472–485 (Eds A. Bationo, B. Waswa, J. Kihara, and J. Kimetu).
Dordrecht: Springer.
McCown, R. L., Hammer, G. L. and Hargreaves, J. N. G. (1996). APSIM: a novel software system for model
development, model testing, and simulation in agricultural systems research. Agricultural Systems 50: 255–271.
Robertson, M. J., Sakala, W., Benson, T. and Shamudzarira, Z. (2005). Simulating response of maize to previous velvet
bean (Mucuna pruriens) crop and nitrogen fertilizer in Malawi. Field Crops Research 91: 91–105.
Rosegrant, M. W., Cai, X. and Cline, S. A. (2002). The role of rain-fed agriculture in the future of global food
production. IFPRI, Environment and Production Technology Division Discussion Paper No. 90.
Shamudzarira, Z. and Robertson, M. J. (2002). Simulating response of maize to nitrogen fertilizer in semi-arid
Zimbabwe. Experimental Agriculture 38: 79–96.
Smaling, E. M. A., Nandwa, S. M., Prestele, H., Roetter, R. and Muchena, F. N. (1992). Yield response of maize to
fertilizers and manure under different agro-ecological conditions in Kenya. Agriculture, Ecosystems and Environment
41: 241–252.
Washington, R., Harrison, M., Conway, D., Black, E., Challinor, A., Grimes, D., Jones, R., Morse, A., Kay, G. and
Todd, M. (2006). African climate change: taking the shorter route. Bulletin of the American Meteorological Society 87:
1355–1366.
