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

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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

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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

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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).

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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

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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.

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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

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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

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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

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Mea

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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.

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-60

-40

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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.

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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.