Assessing the impacts of climate change on maize production within five agroecological zones of Nigeria
by
Alabi, R.T.
International Institute of Tropical Agriculture
Resource and Crop Management Division Crop Ecology and Modelling unit
PMB 5320, Ibadan.
Presented at National Climate Conference Organised by
The Department of Meteorological services Federal Ministry of Aviation
In collaboration with The Nigerian Meteorological Society (NMS)
At Meteorological Conference Centre, Oshodi, Lagos
November 29-Decemebr 1, 1999
2
ABSTRACT
The likely effects of climate change caused by increasing atmospheric carbon dioxide
and temperature levels on maize production in Nigeria were evaluated using a
dynamic crop growth model, CERES-Maize. The model was run under 'fixed
increments' climate scenarios and scenarios predicted for a doubled - CO2 (2xCO2)
atmosphere by the General Fluid Dynamics Laboratory (GFDL), the Goddard
Institute of Space Studies (GISS) and the United Kingdom Meteorological Office
(UKMO) General Circulation Models (GCMs). 20-year historic daily weather data
from 15 sites were used as baseline climate data for the present situation. In general
an increase in CO2 level was found to increase yields while increases in temperature
reduced yields. The humid forest ecology expressed highest yield reduction (18%)
due to increases in temperature while the lowest response (7%) was found in the
derived savannah and southern guinea savannah ecologies. Doubling CO2 only gave
increases in yields at low temperature changes (1−2 °C) whereas higher changes (3−4
°C) tended to counteract effects of a doubled CO2 climate in all the five major maize
growing regions of the country. Moreover, maximum yield decreases of 14%, 10%,
and 8% were simulated under the future climate predicted by UKMO, GISS and
GFDL respectively, for all maize growing regions of Nigeria. Increased temperature
increased crop development rate, which shortened the growing period by about
8−10% in all regions. Precipitation in tropical areas such as Nigeria is generally
suitable for maize production, and so a change in rainfall of about ±20% did not affect
water supply to the crop significantly, although a 20% increase may cause leaching of
essential plant nutrients. Results of this study suggest that rainfed maize production
in Nigeria may decrease by about 10% under a doubled CO2 climate as projected by
the 3 GCMs. Nevertheless, the negative impacts of climate change could be
minimised through adoption of better crop management practices such as earlier
planting and choice of longer maturing varieties.
3
INTRODUCTION
Maize is the third most important crop in the world after wheat and rice. Current
national production of maize is put at 5.2 million tonnes on area of 3.5 million
hectares. However, to meet the demands of its rapidly expanding population, an
estimated 50 % increase in maize production is required over the coming decades
(Wudiri and Fatoba, 1992), a goal that is made difficult by the declining natural
resources. The influence of climate change on maize production in the country,
therefore, only adds to an already complex problem. For this reason, an estimation of
its likely impact is vital in planning strategies to meet the increased demands for
maize in the next century.
In recent years, a number of controlled-environment studies have added to our
understanding of the effects of increased temperature and CO2 on crop growth and
development (Kimball, 1983; Bisbal, 1987; Baker et al., 1989). The use of crop
simulation models is one way in which this knowledge can be extrapolated, not only
outward to a region but also forward in time. And probably represent the best method
we have at present of evaluating the likely effect of climate change. In this study, we
use CERES-Maize; a dynamic crop growth model to investigate the likely effects of a
number of scenarios of changed climate on overall maize production in the five-
agroecological zones of Nigeria.
CERES-Maize model (Jones and Kiniry, 1986) is a sole crop maize model that
predicts growth, development and yield of maize. Its ability to predict maize growth
and yield under diverse conditions all over the world has been verified and found
satisfactory (Hodges, et. al., 1987, Singh, 1985, Sivakumar, 1994, Muchena, 1994,
Jagtap et al., 1993 & 1998). The model has also been improved to facilitate studies of
the effect of climate change on crop performance (Hoogenboom et al. 1995).
In recent years, a number of modelling studies of the likely effects of climate change
on maize production has emerged. Curry et al., (1990) used CERES Maize to
analyse maize response to different climate change scenarios in the southern eastern
USA using 30 year historical data for 19 sites as baseline weather conditions. Their
results showed that maize yield would be reduced below 50% the present level in
4
most of the locations. Similar results were obtained by several other workers in the
US conditions (Rosenzweig, 1989, Cooter, 1990, Easterling et al., 1992 , Mckenney
et al., 1992, Stooksbury and Michaels, 1994). However some authors in other parts
of the world obtained a more beneficial future climate to crop production. For
example Kenny, et al. (1993) observed that a warmer climate was more favourable for
maize production in northern Europe. Moreover, Schulze et al. (1993) used CERES-
Maize to analyse maize productivity in southern Africa, and their results suggested
that there would be increasing maize yields with increasing temperature and CO2.
Furthermore, maize yield increases were predicted for Quebec, Canada (Singh and
Stewart, 1991) under climate change scenarios. Most of these studies not only gave
yield trends in future climate, they also highlighted possible adaptation strategies to
mitigate the effects of climate change on food production in their respective countries.
It is equally important to analyse maize productivity in different ecologies of Nigeria
under future climate scenarios in order to proffer possible adaptive technologies to
food security in the coming millennium. Hence the objective of this study was to
highlight the possible impacts of climate change on maize production and to suggest
possible options for adaptation to future climate in Nigeria.
5
MATERIALS AND METHODS
Crop model description
The CERES-Maize model was designed to simulate crop growth and yield under a
range of soil and climate conditions where maize is normally grown. It predicts crop
phenological development (duration of vegetative and reproductive growth stages) as
affected by cultivar, weather and soils. Photosynthesis and the production and
partitioning of biomass into leaves, stems, roots, and fruit is estimated daily
depending on the crop, soil, and weather factors. A component model of the soil-root
system integrates the effects of rainfall, root growth, and evapotranspiration to predict
day-to-day water availability to the plants and the development of water stress. Water
stress causes reductions in photosynthesis and canopy development, changes in
partitioning of biomass, and increases in senescence or abortion of plant material,
depending on timing and severity of stresses. The model was developed using filed
data from experiments over a range of locations and time.
Jones and Kiniry (1986) described the original CERES-Maize model. Its input
includes the natural system inputs, management, crop and genetic variables. The
natural system inputs consist of weather and soil data and the site latitude. Weather is
considered a non-controlled input consisting of daily solar radiation (MJ/m²/day),
maximum and minimum air temperature (° C), and rainfall (mm/day). Daylength is
computed from day-of-year and latitude. The soil parameters includes the soil albedo,
a soil water drainage rate constant, upper limit of stage 1 soil evaporation (mm), a
runoff curve number, and characteristics describing each layer in a one-dimensional
profile. For each soil layer, the layer depth, lower limit of plant extractable water,
drained upper limit of soil water, and saturated water content are prescribed inputs. In
addition, a root growth-weighting factor is a specific input for each soil layer for use
in distributing new root growth as the season progresses.
The management inputs are the beginning day of the simulation, planting day, plant
density (plants/m²), row spacing (m), depth of planting (cm). It also includes irrigation
information such as schedule, day and amount of irrigation. Irrigation could be fixed
or applied automatically when certain conditions are met. Other management
parameters such as fertiliser application date, type and amount could be specified.
6
Sowing dates could be specified or the model could be asked to plant when certain
soil moisture conditions are attained. The crop and genetic inputs include cultivar
characteristics such as cultivar's sensitivity to photoperiod, thermal duration of
phenological and reproductive stages and biomass coefficients.
Climate change scenarios
Climate change scenarios for Nigeria were formulated using outputs from the three
Global Circulation Models (GCMs) for a doubled CO2 climate. These GCMs produce
monthly mean data for the variables of interest, and have various spatial resolutions.
The GCM of the Goddard Institute for Space Studies (GISS) uses 4 x 5 ° grid boxes
while that of Geophysical and Fluid Dynamics Laboratory (GFDL) uses 4 x 7.5 °
(latitude by longitude) grid boxes. The third GCM used is the United Kingdom
Meteorological Office (UKMO) with a similar resolution to that of GISS model.
Monthly average change for temperature, rainfall and solar radiation under a doubled
CO2 atmosphere were obtained from the GCMs for the appropriate grid boxes for the
five different agroecological zones of Nigeria. Adding the appropriate monthly
changes to the daily temperature and solar radiation values for the historical years'
data generated the new daily temperature and solar radiation sequences. For rainfall,
the historical daily data were multiplied by the appropriate monthly ratios of changes
in precipitation under doubled CO2 climate to the mean monthly baseline data. This
approach is not capable of modifying the sequence of rain-free and rainy days within
a month, although this normalisation assumed that, while GCMs may not reproduce
the observed present day climate very closely, the change between 1 x CO2 and 2 x
CO2 equilibrium conditions is representative of the difference between present climate
and a future climate following an equivalent doubling of CO2 (Parry et. Al, 1988).
The Simulation experiment
Simulations for 15 sites, three in each agroecological zones, (AEZ) over 20-historical
weather year (1971-1990) were made using the Strategy Evaluation program of the
Decision Support System for Agrotechnology Transfer (DSSAT) version 3.5.
Potential maize yields were simulated under 19 different scenarios of changed
climate, including 'fixed increment' changes in CO2 (1 x CO2, 2 x CO2), temperatures
(+1, +2, +3, +4 °C above current temperature). Rainfall changes of +20% and -20%
7
the current levels under doubled CO2 climate was also considered. Potential yields
were also simulated under the three GCM scenarios predicted for a doubled-CO2
climate (2 x CO2).
The maize variety used, TZBSR-W, is a medium duration (100-110 days), open-
pollinated, and streak resistant, and is widely grown or recommended for commercial
cultivation in the savannahs (MIP, 1995; Carsky and Kling, 1997). The genetic
coefficient inputs required by the model for TZBSR-W were obtained from Jagtap et
al. (1998). Optimum planting time used in the study for each of the five AEZ of
Nigeria were obtained from Alabi, 1999. Soil properties inputs required by the model
were obtained from the Federal Department of Agricultural Land Resources (1990).
8
RESULTS AND DISCUSSION
Current and predicted climate change in Nigeria.
The anticipated global climate change has been discussed in several publications and
summarised in the Intergovernmental Panel on Climate Change (IPCC) report
(Houghton et al., 1990). Figure 1 shows long-term mean air temperature for two
extreme ecology (humid forest and semi-arid) in Nigeria and the predictions for a
future climate in which CO2 is doubled the present level as presented by the three
GCM; UKMO, GISS and GFDL. At Kano, in the semi-arid environment, the
predicted temperature change was highest by the UKMO especially during the month
of January to April. Predicted changes in temperature for the semi-arid region during
the growing season (June to September) was similar for the three models, the gap
being between 2 and 2.5 °C. At Onne, near Port Harcourt in the humid forest
ecology, GFDL predicted the warmest climate for most months while GISS gave the
least changes. The gap between the present and the future climate was always
constant for all months and for all models. The mean change predicted by UKMO was
2.3 °C with a standard deviation (STD) of 0.43, by GISS was 1.4 °C with a STD of
0.8, and that of GFDL was 2.3 °C with a STD of 0.32. This result is in line with the
findings of Magadza (1994) who found that the UKMO gave the severest temperature
changes within the southern Africa region.
Predicted changes in precipitation for the five AEZ of Nigeria is presented in Figure
2. It can be seen that all models predicted a drier dry season for all ecologies of
Nigeria except a little increase for February by the GFDL model. UKMO was the
only model that gave significant increases in monthly rainfall during the growing
season for most of the ecological regions. Length of growing period could only be
seen extended in the semi-arid ecological zone. Amount of rainfall remarkably
increased for all months within the normal growing period (April to October), for the
savannahs and the forest ecology. This result suggests likely increase in soil erosion
and plant nutrient losses due to leaching. The GISS and GFDL models generally
predicted a declined rainfall for most of the growing season in all ecological regions
suggesting more stressful conditions for crops. This effect might be especially more
pronounced in the northern guinea savannah and semi-arid in which precipitation
levels might just be marginally sufficient for most food crops grown in the area.
9
Effect of fixed increments of temperature, rainfall and CO2 on potential yields
Table 1 shows the overall effect predicted by CERES-Maize of changes in
temperature, rainfall and CO2 levels on potential yields averaged in all agroecological
zones (AEZ) for all available years. Increases in temperature at current CO2 level
caused a general decline in yields, the degree of which increased with temperature.
These effects were more pronounced in the humid forest and semi-arid AEZ
(13−18%) while they were negligible in the savannahs (7%). A doubling of CO2 level
had a counteracting effect to that of temperature at low temperature increases
(1−2°C). At higher temperature increases, however, doubling CO2 levels could no
longer compensate for yield reduction due to increasing temperature. In general, crop
responds directly to increased CO2 with increased rates of photosynthesis and reduced
transpiration resulting in increasing yields (Rogers et al. 1983). From Table 1, yield
increases of between 5−7% were due to doubling CO2 over the current levels. Similar
beneficial trends of increased atmospheric CO2 concentration have been reported by
many authors in diverse ecologies (Acock, 1990, Curry et al., 1990, Stockle et al.
1992). These trends have been confirmed with filed experiments using open top
chambers (Dalhman et al. 1985, Tubiello et al. 1999). Tubiello et al. (1999), in an
elevated CO2 experiment (Free-Air Carbon dioxide Enrichment (FACE)) to validate
CERES-Wheat in a semi-arid site of the US found a good correlation between model
predictions and the experimental data. Their results further confirmed the ability of
the generic model CERES to simulate the effects of rising atmospheric CO2
concentrations and associated climate change on crop yields. For the future climate
of doubled CO2, changes in rainfall amount of ±20% the present level did not have
any significant impact on maize yield (Table 1). This is not unexpected in most
ecologies of Nigeria where seasonal rainfall is not a limiting factor to maize
production. Furthermore, under an elevated CO2, stomatal conductance and
transpiration per unit leaf area is reduced. Kimball & Idso, 1983 quantified average
transpiration reduction of 34% for maize and some other C3 crops. However,
simulated cumulative evapotranspiration was reduced by 16-20% (data not shown) in
this study. This implies that water use efficiency of maize is enhanced under an
elevated CO2 atmosphere. Maize requires 400-500 mm of rainfall for optimum
performance (Mornu, 1999) which is below the seasonal rainfall level of most AEZ
10
of Nigeria, especially the savannahs and the humid forest. An increase in rainfall
beyond the present level may therefore lead to soil nutrient depletion through leaching
and erosion. Improvement of water use efficiency in a future climate may be
beneficial for dryland crops.
Temperature changes have negative influence on maize phenology (Table 2). The
time to the first reproductive phase (anthesis) decreased by 2−4 days for all ecologies
and crop physiological maturity duration was shortened by 4−12 days. These
phenological events were responsible for yield reduction due to increased carbon
assimilation rate with higher temperature. This observation is similar to the findings
of many other workers (Brown and Rosenberg, 1997, Karim et al, 1996).
Effect of predicted GCM scenarios on potential yields
The simulated yield changes for all sites under the three GCM scenarios are presented
in Table 3. In the humid forest ecology, the model predicted the highest yield
reduction (12−16%) under UKMO scenario and lowest under GFDL (6−8%).
However, the yield predictions under GFDL was not remarkably different from that
simulated under the GISS conditions (8−12%). At Ibadan, a site within the derived
savannah zone, simulated yield increased by 0.1−6% for the three GCM scenarios.
Other sites in the derived savannah ecology displayed yield reduction of about
4−11%. Sites within southern and northern guinea savannah ecologies exhibited
similar yield responses to predicted climate change scenarios by the three GCMs, the
highest reduction being obtained under UKMO. For all the sites in the semi-arid
zone, simulated yields decreased in similar patterns to that observed in the humid
ecology, UKMO scenario gave the highest while GFDL showed the least changes.
Wang et al, (1996) obtained similar yield decreases with the 3 GCM while simulating
maize production in China using CERES-Maize. Maize yield decreases of similar
order under GCM scenario were simulated for maize growing areas of Zimbabwe
(Muchena and Iglesias, 1995).
11
ADAPTATION OPTIONS
Adjustment of planting dates
Five locations, one for each AEZ, (Onne, Ibadan, Mokwa, Kaduna and Kano) were
selected to investigate how the warmer climate predicted by UKMO under a doubled
CO2 scenario would influence crop planting dates for optimum yields. Maize was
planted 12 times (middle of every month) a year under rainfed conditions for the
current climate and the UKMO scenario. The results of 20-year simulations are
presented in Figure 3. Under the present climatic conditions, the simulated optimum
planting time for the derived savannah ecology was April, although March and July
planting gave yields that were not significantly different from the optimum (7%).
However, under the UKMO climate change scenario, earlier planting of March gave
the optimum yield, which did not differ remarkably (5%), from those obtained for
April and July sowings. In the southern guinea savannah, the best planting time under
the present climate was May, followed by June, April, and July whereas this order
was slightly shifted to April, May, June and July under the UKMO scenario. The
northern guinea savannah ecology gave the optimum yields with April and May
sowings under the present climatic conditions, while the best planting time for the
future climate as projected by UKMO were April and August, which were closely
followed by May, June and July. The semi-arid ecological region exhibited optimum
planting times of June and May under both the present climate and the UKMO
predicted conditions of a future climate. All months of the year seemed appropriate
for maize cultivation in the humid forest ecology under both present and future
climate, although the optimum yields were obtained during October sowing. The
maize-sowing window for each AEZ was not significantly influenced by climate
change conditions. Planting date has been proffered as one of the adaptation strategies
to mitigate the impacts of climate change on maize production (Seino, 1995). In
Nigeria, shifts in planting times may not influence yields much as suggested by the
outcome of this study.
Longer maturing varieties
Adaptation to varieties was simulated for five representative sites (Onne, Ibadan,
Mokwa, Kaduna, Kano) each within one AEZ of Nigeria. It was assumed that the
main difference between early and late maturing varieties is the length of the basic
12
vegetative period before flowering. It has been found for many varieties that the
durations of the flowering and the grain-filling periods, when corrected for
temperature differences are similar, regardless of total crop duration (Vergara and
Chang, 1985). The thermal time (base 8°C) from emergence to silking of maize
cultivar TZSBR-W now referred to as TZ-Medium was found to be 350-degree days
(Dd) (Jagtap et al., 1993). This coefficient was modified to form the three species of
TZSBR-W, which have similar growth characteristics except the duration of basic
vegetative period following the technique of Mathews et al., (1997). The cultivars so
formed are called TZ- Early with a coefficient of 200 Dd, TZ-Late, 400 Dd, and TZ-
V.Late, 480 Dd.
Results of simulated yields and days to physiological maturity of the four maize
cultivars under the current climate and UKMO scenario are presented in Table 4 and
5. As total crop duration increased, simulated yields increased from a mean of 4.14
t/ha to 5.82 t/ha under the present climatic conditions. A similar trend was portrayed
under the UKMO scenario, although yields were generally reduced due to warmer
climatic conditions. However, it is interesting to note that the yields of the TZ-V.Late
under UKMO scenario were similar to that of TZ-Medium under the present climate.
Moreover, the duration to physiological maturity of the very late maturing variety
under the UKMO climate change scenario was equivalent to that of the medium
maturing variety under the present climate. This implies that for the same maturity
duration, present yield level of the cultivar TZ-Medium could be maintained under a
future climate if the cultivar TZ-V.Late was planted instead. These results suggest
that the yield reducing tendencies of a hotter climate could be mitigated through
appropriate choice of cultivar.
SUMMARY AND IMPLICATIONS OF RESULTS.
The three GCMs predicted varying changes in temperature, and precipitation for all
ecologies of Nigeria. The radiation changes predicted by the 3 models for most of
Nigeria was negligible to make any impact on crop growth (< ±0.2 MJ/m²/day).
Precipitation changes predicted by UKMO may lead to greater soil erosion and loss of
soil fertility, which portends grave consequences for agriculture in the savannah, and
humid forest ecologies of Nigeria.
13
Increases in temperature accelerated the phenological development for maize,
shortened time to maturity, lowered yields, and decreased water use efficiency.
However, increases in CO2 and consequent increases in leaf area index and stomatal
resistance increased crop yield and water use efficiency, lessening any negative
impacts of changes in temperature.
Adjustments to management practices may also help to offset any detrimental effects
of climate change on maize production. Shifts in planting time may improve maize
yield performance especially in the savannah ecology of Nigeria under an elevated
CO2 atmosphere. The results also showed that choice of longer maturing varieties in
the future climate could restore yield levels to those predicted for current climates.
Some care is necessary, however, in interpreting results from scenarios predicted by
GCMs. Their most significant limitations are their poor spatial resolution, inadequate
coupling of atmospheric and oceanic processes, poor simulation of cloud processes
(Mearns, 1990) and inadequate representation of the biosphere and its feedbacks
(Dickinson, 1989). Most GCMs have difficulty in even describing current climate
adequately, particularly precipitation (Bachelet et al., 1993), let alone climates several
decades hence. Neither are current GCMs able to predict changes in the variability of
the weather or the frequency of catastrophic events such as hurricanes, floods or even
the monsoons, all of which can be just as, or even more, important in determining
crop yields as the average climatic data. It seems, therefore, that GCMs can at best be
used to suggest the likely direction and rate of change of future climates.
Nevertheless, despite the limitations imposed by the assumptions made in both the
GCM and the crop simulation models, the current study marks significant progress in
our understanding of how future climates are likely to affect maize production in
Nigeria. The use of simulation models to predict the likely effects of climate change
on crop production is relatively new in West Africa. It is hoped that this study can be
used as a baseline for future studies in which some of the current limitations are
addressed so that increasingly more accurate predictions can be made.
14
REFERENCES
Acock, B. 1990. Effects of carbon dioxide on photosynthesis, plant growth, and other
processes, In: Impact of Carbon Dioxide, Trace Gases, and Climate Change
on Global Agriculture, eds B.A. Kimball, N.J. Rosenberg and L.H. Allen, Jr.
ASA, Madison, WI, USA, Special publication No. 53, pp 45−60.
Alabi, R.T. 1999. Optimising regional productivity of maize in Nigeria using GIS and
Decision Support System. M.Sc. thesis. Department of Geography, University
of Ibadan, Ibadan, Nigeria. 46 pp.
Baker, J.T., Allen L.H., Boote, K.J., Jones, P., Jones, J.W. 1989. Response of soybean
to air temperature and carbon dioxide concentration. Crop Sci. 29:98−105.
Batchelet, D., Van Sickle, J., Gay, C.A. 1993. The impact of climatic change on rice
yield: Evaluation of the efficacy of different modelling approaches. In:
Systems Approaches for Agricultural Development, ed. F.W.T. Penning de
Vries, P.S. Teng and K Metselaar. Kluwer Academic Publishers, Dordrecht,
The Netherlands, pp. 145−174.
Bisbal, E.C., 1987. Effects of subambient and superambient carbon dioxide levels on
growth, development and total non-structural carbohydrate of soybean. M.Sc.
thesis, Agronomy Dept., Univ. of Florida, Gainesville.
Brown, R.A., Rosenberg, N.J. 1997. Sensitivity of crop yield and water use to change
in a range of climatic factors and CO2 concentration: a simulation study
applying EPIC to the central USA. Agric. and For. Mete. 83(3−4):171−203.
Carsky, R. J. and Kling, J. G.(1997). Realization of maize yield potentials on
farmers’ fields- possibilities and problems. In: Contributing to food self-
sufficiency: Maize research and development in west and central Africa, eds.
B. Badu-Apraku, M.O., Akoroda, M., Ouedraogo and Quin, F.M. IITA-
WECAMAN, Ibadan,Nigeria, pp 12−21.
Cooter, E.J., 1990. The impact of climate change on continuos corn production in the
southern USA. Climatic change. 1990. 16(1): 53−82.
Curry, R.B., Peart, R.M., Jones, J.W., Boote, K.J., Allen, Jr. L.H., 1990. Simulation
as a tool for analysing crop response to climate change. Transactions of the
ASAE, 33(3): 981−990.
15
Dahlman, R.C., Strain, B.R., Rogers, H.H., 1985. Research on the response of
vegetation to elevated atmospheric carbon dioxide. J. of Envir.Qual. 14: 1−8.
Dickinson, R.E. (1989). Uncertainties of estimates of climatic change: a review.
Climatic Change. 15: 5−13.
Easterling, W.E., Mckenney, M.S., Rosenberg, N.J. Lemon, K.M. 1992. Simulations
of crop response to climate change; effects with present technology and no
adjustments (the dumb farmer scenario). Agric. and For. Meteo. 59: 53−73.
Federal Department of Agricultural Land Resources, 1990. The reconnaissance soil
survey of Nigeria. Soils report (five volumes).
Hodges, T., Botner, D., Sakamoto, C. And Haug, J., 1987. Using CERES-Maize
model to estimate production for the US Cornbelt. Agric. and For. Met.
40:293-303.
Hoogenboom, G., Tsuji, G.y., Pickering, N.B. Curry, R.B., Jones, J.W., Upendra, S.,
Godwin, D.C., Singh, U. 1995. Decision support system to study climate
change impacts on crop production. Climate change and agriculture: Analysis
of potential international impacts. Proceedings of a symposium sponsored by
the American Society of Agronomy in Minneapolis, MN, 4−5 Nov. 1992.
Organised by Division A-3 (Agroclimatology and Agronomic Modelling) and
Division A-6 (International Agronomy). ASA-Special-Publication. 1995 No.
59, 51−75.
Houghton, J.T., Jenkibs, G. C., Ephraums, J.J. 1990. Climate Change: The IPCC
Scientific Assessment Cambridge University press, Cambridge.
Jagtap, S.S., Alabi, R.T., Adeleye, O. 1998. The influence of maize density on
resource use and productivity: An experimental and simulation study. African
Crop Sci. J. 6(3): 259−272.
Jagtap, S.S., Mornu, M. and Kang, B.T. 1993. Simulation of growth, development
and yield of maize in the transition zone of Nigeria. Agricultural systems
41:215−229.
Jones, C.A. Kiniry, J.R.1986. CERES-Maize: A simulation Model of Maize Growth
and Development. College Station: Texas A&M University press.
Karim, Z., Hussain, S.G., Ahmed, M., Erda, L 1996. Assessing impacts of climatic
variations on foodgrain production in Bangladesh. Special Issue: Climate
change vulnerability and adaptation in Asia and the Pacific. Workshop held in
16
Manila, Philippines, 15-19 January 1996. Water, Air and soil pollution.
92(1−2): 53−62.
Kenny, G..J. Harrison, P.A., Olesen, J.E., Parry, M.L. 1993. The effects of climate
change on land suitability of grain maize, winter wheat and cauliflower in
Europe. European Journal of Agronomy. 2(4): 325−338.
Kimbal, B.A. 1983. Carbon dioxide and agricultural yield: An assemblage and
analysis of 430 prior observations. Agron. J. 75:779−788.
Kimball, B.A., Idso, S.B., 1983. Increasing atmospheric CO2:. Effects on crop yield
water use and climate. Agricultural Water Management, 7:55−72.
Magadza, C.H.D. 1994. Climate change: some likely multiple impacts in southern
Africa. Food Policy 19(2):165−191.
Maize Improvement Program (MIP). (1995). Maize Improvement Program Archival
Report. International Institute of Tropical Agriculture.
Mathews, R.B., Kropff, M.J., Horie, T., Bachelet, D. 1997. Simulating the impact of
climate on rice production in Asia and evaluating options for adaptation.
Agricultural Systems, 54(3): 399−425.
Mckenney, M.S., Easterling, W. E., and Rosenberg, N.J. 1992. Simulation of crop
productivity and responses to climate change in the year 2030: the role of
future technologies, adjustments and adaptations. Agric. and For. Met., 59:
103−127.
Mearns, L.O. 1990. Future directions in climate modelling: A climate impacts
persepectives. In: Climatic change: Implications for water and ecological
resources, ed. G. Wall and M. Sanderson. Proceedings of an International
symposium, Department of Geography, University of Waterloo, Canada, pp.
51−58.
Mornu, M.E. 1999. Water use studies for selected crops in the transitional humid
zone of Nigeria. Ph.D thesis, Dept. Geography, University of Ibadan. Ibadan,
Nigeria.
Muchena, P. (1994). Experiences of developing countries in using crop modeling
predictions. In: Uhlir, P.F. and Carter, G.C. (Eds.) Crop modeling and related
environmental data. A focus on application for Arid and Semi-arid regions in
developing countries. CODATA Monograph Series, Volume 1.
17
Muchena, P. Iglesias, A. 1995. Vulnerability of maize yields to climate change in
different farming sectors in Zimbabwe. Climate change and agriculture:
Analysis of potential international impacts. Proceedings of a symposium
sponsored by the American Society of Agronomy in Minneapolis, MN, and 4-5
Nov. 1992. Organised by Division A-3 (Agroclimatology and Agronomic
Modelling) and Division A-6 (International Agronomy). ASA-Special-
Publication. 1995 No. 59, 229−239.
Parry, M.L., Carter , T.R., Konijn, N.T. (eds). (1988). The impact of Climatic
Variations on Agriculture. Kluwer Academic Publishers, Dordrecht, The
Netherlands.
Rogers, H.H., Bingham, G.E., Cure, J.D., Smith, J.M., Surano, K.A. 1983. Responses
of selected plant species to elevated carbon dioxide in the field. Journal of
Environmental Quality. 12:569−574.
Rosenzweig, C. 1989. Climate change and CO2 effects on wheat and corn in the
Great Plains. 19th conference on agricultural and forest meteorology and
the ninth conference on biometeorology and aerobiology, March 7−10, 1989.
Charleston, South Carolina. 1989, J26−J29.
Schulze, R.E., Kiker, G.A., Kunz, R.P., 1993. Global climate change and agricultural
productivity in southern Africa. Global Environmnetal Change (USA). 3(4):
330−349.
Seino, H. 1995. Implication of climate change for crop production in Japan. Climate
change and agriculture: Analysis of potential international impacts.
Proceedings of a symposium sponsored by the American Society of Agronomy
in Minneapolis, MN, and 4-5 Nov. 1992. Organised by Division A-3
(Agroclimatology and Agronomic Modelling) and Division A-6 (International
Agronomy). ASA-Special-Publication. 1995 No. 59, 293−306.
Singh, B. Stewart, RB. 1991. Potential impacts of a CO2-induced climate change
using the GISS scenario on Agriculture in Quebec, Canada. Agriculture,
Ecosystems and Environment. 1991, 35(4): 327−347.
Singh, U. 1985. A Crop Growth Model for Predicting Corn (Zea mays L.)
Performance in the Tropics. Ph.D. Dissertation Department of Agronomy and
Soil Science, College of Tropical Agriculture and Human Resources,
University of Hawaii, Honolulu. 410 pp
18
SivaKumar, M.V.K. (1994). Applications of crop modelling in Arid and Semiarid
regions. In: Uhlir, P.F. and Carter, G.C. (Eds.) Crop modelling and related
environmental data. A focus on application for Arid and Semi-arid regions in
developing countries. CODATA Monograph Series, Volume 1.
Stockle, C.O., Williams, J.R. Rosenberg, N.J., Jones, C.A. 1992. A method for
estimating the direct and climatic effects of rising atmospheric carbon dioxide
on growth and yield of crops. Modification of the EPIC model for climate
change analysis. Agricultural Systems, 38(3): 225−238.
Stooksbury, D.E., Michaels, P.J. 1994. Climate change and large area corn yield in
the southern united States. Agronomy Journal. 86(3): 564−569.
Tubiello, F.N., Rosenzweig, C., Kimball, B.A., Pinter, Jr. P.J., Wall, G.W.,
Hunsaker, D.J., LaMorte, R.L., Garcia, R.L. 1999. Testing CERES-Wheat
with free-air carbon dioxide enrichment (FACE) experimental data: CO2 and
Water interactions. Agronomy Journal. 91:247−255.
Vergara, B.S., Chang, T.T. 1985. The flowering response of the rice plant to
photoperiod . A review of the literature. International Rice Research Institute,
Los Banos, Philipines, 61 pp.
Wang, JingHua, Erda, L., Wang, J.H. Erda, L et-al. 1996. The impacts of potential
climate change and climate variability on simulated maize production in
China. Special Issue: Climate change vulnerability and adaptation in Asia and
the Pacific. Workshop held in Manila, Philippines, 15-19 January 1996.
Water, Air and soil pollution. 92(1-2): 75−85.
Wudiri, B.B., Fatoba, I.O., 1992. Cereals in the food economy of Nigeria in:
Proceedings of the Workshop on recent Developments in Cereal Production in
Nigeria held in Durbar hotel, Kaduna, 2-4 September 1991.S.M. Lawani, T.
Babaleye (Eds.)
19
Table 1: Mean predicted changes (%) in yields under the 'Fixed' Temperature and CO2 scenarios: Changes are averaged across agroecological zones and 20 years
Temperature increments
330 ppm (1 x CO2) 1 °C 2 °C 3 °C 4 °C Mean Humid Forest -6.2 -15.8 -20.8 -28.6 -17.8 Derived Savannah -2.4 -4.6 -7.7 -11.8 -6.7 Southern Guinea Savannah -2.6 -4.3 -7.4 -11.7 -6.5 Northern Guinea Savannah -5.6 -9.6 -14.4 -19.7 -12.3 Semi Arid -4.2 -9.0 -15.4 -21.6 -12.6 660 ppm (2 x CO2) Humid Forest 1.5 -8.2 -13.5 -22.0 -10.5 Derived Savannah 1.6 -0.5 -2.5 -6.6 -2.0 Southern Guinea Savannah 2.5 0.9 -2.2 -5.2 -1.0 Northern Guinea Savannah 0.4 -3.5 -8.3 -13.3 -6.2 Semi Arid 2.1 -2.8 -9.1 -14.3 -6.0 2 x CO2 +20% rain Humid Forest 1.4 -8.1 -13.2 -21.7 -10.4 Derived Savannah 1.5 -0.2 -1.8 -5.9 -1.6 Southern Guinea Savannah 3.2 1.5 -1.5 -4.7 -0.4 Northern Guinea Savannah 0.0 -3.7 -8.4 -13.4 -6.4 Semi Arid 1.6 -3.3 -9.5 -14.6 -6.4 2 x CO2 -20% rain Humid Forest 1.6 -8.2 -13.7 -22.1 -10.6 Derived Savannah 0.0 -2.2 -4.2 -8.9 -3.8 Southern Guinea Savannah 0.9 -0.9 -3.7 -7.3 -2.7 Northern Guinea Savannah 0.5 -3.4 -8.4 -13.4 -6.2 Semi Arid 2.5 -2.7 -9.0 -14.2 -5.9
Table 2: Mean predicted changes (days) in number of days to key phenological events under 'Fixed' Temperature scenarios
Temperature increments
1 °C 2 °C 3 °C 4 °C Mean Flowering Humid Forest -1.7 -2.2 -4.0 -4.4 -3.0 Derived Savannah -1.9 -2.4 -4.6 -5.4 -3.6 Southern Guinea Savannah -1.7 -2.3 -4.2 -4.4 -3.1 Northern Guinea Savannah -1.6 -2.3 -3.9 -3.4 -2.8 Semi Arid -2.0 -1.5 -3.1 -2.5 -2.3 Maturity Humid Forest -3.5 -6.0 -9.3 -9.8 -7.1 Derived Savannah -4.1 -6.6 -10.7 -11.9 -8.3 Southern Guinea Savannah -3.8 -6.3 -10.0 -11.3 -7.8 Northern Guinea Savannah -3.8 -5.9 -9.3 -10.7 -7.4 Semi Arid -3.5 -4.8 -8.3 -9.5 -6.5
20
Table 3: Predicted maize yields in the five agroecological zones of Nigeria under the three GCM scenarios of doubled CO2 climate
Yield Percentage change in yields
Site Agroecological Zones (Kg/ha) UKMO GISS GFDL Benin Humid Forest 4370 -13.9 -9.5 -6.1 Onne 4426 -12.0 -8.1 -8.1 Warri 4382 -15.9 -12.2 -7.9 Ibadan Derived Savannah 4710 0.1 3.5 6.1 Lokoja 4314 -4.4 -1.5 0.2 Markurdi 6016 -7.0 -11.0 -4.9 Mokwa Southern Guinea Savannah 6317 -6.7 -9.6 -4.8 Minna 5286 -11.0 -5.0 -0.7 Yola 4535 -10.0 -5.0 -0.7 Kaduna Northern Guinea Savannah 6451 -4.7 -2.1 0.4 Yelwa 5098 -9.7 -4.6 -0.7 Bauchi 5523 -8.7 -4.8 -0.2 Sokoto Semi Arid 4947 -10.8 -8.7 -4.7 Kano 6093 -11.0 -7.4 -4.2 Maiduguri 5825 -13.8 -9.5 -6.4
Table 4: Simulated yields(kg/ha) of four maize cultivars under current climate and UKMO
2xCO2 predicted climate Variety Early Medium Late Very late Current climate Humid Forest 2792 4071 4234 4547 Derived Savannah 3951 4630 4646 4542 Southern Guinea Savannah 4649 6364 6421 6684 Northern Guinea Savannah 4862 6417 6574 6718 Semi Arid 4444 6042 6421 6583 Mean 4140 5505 5659 5815 UKMO 2 x CO2 Predicted Climate Humid Forest 2393 3549 3854 3968 Derived Savannah 3786 4610 4548 4571 Southern Guinea Savannah 3994 5884 5898 6488 Northern Guinea Savannah 4592 6077 6175 6438 Semi Arid 3897 5591 6056 6042 Mean 3732 5142 5306 5501
21
Table 5: Simulated mean days to physiological maturity of 4 maize cultivars under current climate and UKMO 2xCO2 predicted climate Variety Early Medium Late Very late Current climate Humid Forest 84 99 104 112 Derived Savannah 92 109 114 123 Southern Guinea Savannah 94 112 116 125 Northern Guinea Savannah 99 116 123 131 Semi Arid 92 106 111 119 Mean 92 108 113 122 UKMO 2 x CO2 Predicted Climate Humid Forest 75 87 93 98 Derived Savannah 79 94 99 107 Southern Guinea Savannah 81 96 100 110 Northern Guinea Savannah 85 101 105 114 Semi Arid 80 94 99 106 Mean 80 94 99 107
22
20
22
24
26
28
30
32
34
36
Jan Feb M ar Apr M ay June July Aug Sep Oct Nov Dec
Mea
n A
ir Te
mpe
ratu
re (°
C)
Kano(Semi-arid)
24
25
26
27
28
29
30
31
Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec
Mea
n Ai
r Tem
pera
ture
(°C
)
NormalUKMOGISSGFDL
Port HarcourtHumid forest
Fig. 1: Normal (1961-1990 mean) and predicted temperature by UKMO, GISS, and GFDL for a doubled CO2 climate for two sites in humid and Semi-arid AEZ of Nigeria.
23
-60
-40
-20
0
20
40
60
80
100
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Cha
nge
in p
reci
pita
tion
(mm
) UKMO
-30
-20
-10
0
10
20
30
40
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Cha
nge
in p
reci
pita
tion
(mm
) GISS
-80
-60
-40
-20
0
20
40
60
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Cha
nge
in p
reci
pita
tion
(mm
)
HF DS SGS NGS SA
GFDL
Fig. 2: Predicted changes in precipitation for a doubled CO2 climate for the five AEZ of Nigeria by the GISS, UKMO, and GFDL Global Circulation Models.
24
0
1000
2000
3000
4000
5000
6000
7000
8000
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Mea
n m
aize
yie
lds
(kg/
ha)
0
1000
2000
3000
4000
5000
6000
7000
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Mea
n m
aize
yie
ld (k
g/ha
)
Humid ForestDerived savannaSouthern Guinea SavannaNorthern Guinea savannaSemi-arid
Fig. 3: Simulated maize yields for 12 planting dates under rainfed conditions for (a) the present climate (1971-1990) and (b) future climate (UKMO scenario of 2xCO2) for all agroecological zones in Nigeria.
Top Related