Transcript food security and climate change in sub saharan west africa

FOOD SECURITY AND CLIMATE CHANGE
IN SUB SAHARAN
WEST AFRICA
INTRODUCTION
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One of the problems of development in Sub Saharan West Africa
region of west Africa pertains to food insecurity. Research needs to
address this both as a means of improving food productivity in the
present and in the future when the climatic conditions maybe less
favorable for agricultural purposes
THE CONTEXT
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What is potentially at stake providing the justification for this study is the social,
cultural and economic development of West Africa, based on a sustainable use of the
resources of the environment.
It is not a subject for contention, that every human being is entitled to, and should
have access to the fruits of development which include: adequate food, clean water
and energy, safe shelter, a healthy home environment, qualitative education, and
satisfying employment.
However, notwithstanding the spectacular gains in the means of development, such
as the advances in science, technology and medicine during the just concluded
century, the process has been skewed to the detriment of certain major regions of
the world.
Sub Saharan West Africa is probably the least developed of the world’s major
regions going by the statistics available at the end of the 20th Century. Moreover the
prospect for the type of accelerated development needed to bridge the gap between
this region and the other regions is not bright.
FOOD INSECURITY 1
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There has been an upward trend in
national food production during the last
decade.
However, because of a high rate of
population increase, food availability per
capita has declined
Compared with the developed countries,
nutritional standard is still very low.
FOOD INSECURITY 2
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Average national dietary energy deficit varies
between 210 and 390 kg/person compared with
110 to 160 in the developed countries.
With the exception of Nigeria, all countries
received food aid in1999.
From 1994 t0 1999, no country in the sub
continent was self sufficient in cereal production.
SORGHUM: AREA HARVESTED BY STATE
Sorghumg.shp
0-5
5 - 183
183 - 460
460 - 864
864 - 1369
'000 ha
"
"
"
"
N
W
300
0
300
E
600 Kilometers
S
MAIZE: AREA HARVESTED BY STATE
Maize.shp
1.2 - 18
'000 tonnes
18 - 47
'000 tonnes
47 - 122
'000 tonnes
122 - 240
'000 tonnes
240 - 437
'000 tonnes
N
W
300
0
300
E
600 Kilometers
S
Rice: Area Harvested by State
Rice: Area Harvested by State
0-1
'000 hectares
2-7
''
8 - 12
''
13 - 20
''
21 - 44
''
N
W
300
0
300
E
600 Kilometers
S
Millet: Area harvested by state
Millet.shp
0 - 1.8
"000" hectares
1.8 - 41
"
41 - 114
"
114 - 761
"
761 - 1398
"
N
W
300
0
300
E
600 Kilometers
S
CLIMATE VARIABILITY 1
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Interannual variation in monthly temperature and
radiation is very low compared with precipitation
Coefficient of variation of monthly temperature
and radiation is usually less than 5%
Coefficient of variation of monthly precipitation
can be as high as 500%
Coefficient of variation of monthly precipitation is
lowest during the wet season and in the wetter
southern areas than in the north
In essence climate variability means
precipitation variability
CLIMATE VARIABILITY 2.
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There has been a general trend towards aridity
in most of the stations studied;
All the rainfall time series, when smoothed with
the 5-year moving average, reveal patterns
characterized by oscillations;
The fluctuations demonstrate some periodic
tendencies which are regular in nature;
The fluctuations are also characterized by
strong persistence and temporal dependencies;
CLIMATE VARIABILITY 3
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There appears to be a general lack of
correspondence in the patterns of the
fluctuations between seasons. In other words, a
wet March-April-May is not necessarily followed
by a wet June-July-August.
Also, there appears to be regional variations in
terms of the rainfall fluctuations. In other words,
dry years in one region are not necessarily dry
years in other regions.
CROP MODEL APPLICATIONS
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There are five different ways in which Epic Crop
Model could be employed. These include:
Estimation of productivity
Estimation of total production
Assessments of impacts of environment factors
Assessment of vulnerability
Assessment of adaptation options
DEFINITIONS
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Crop productivity is the economic yield usually
expressed as yield per hectare.
Crop production is simply the total amount of
seeds, grain or tuber for which a unit area is
responsible.
Impact is the change observed in the form or
function of a biophysical or human system as a
result of a change in the environment.
DEFINITIONS CONT
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Vulnerability expresses the probability that
a human or a biophysical system falls into
a state of disaster as a result of
environmental changes.
Adaptations are the adjustments, which
have to be made to crop production
systems in order to live successfully with a
changed climate.
LIMITATIONS OF CROP MODEL
IN CROP-CLIMATE STUDIES
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To be able to successfully estimate crop
production and productivity, model output must
be a credible substitute for observed values.
For vulnerability assessment, the model must
be capable of accurately estimating yields
corresponding to various annual weather
patterns.
What is needed for the assessment of impacts
of climate variability is the difference between
pre impact and post impact productivity and
production.
LIMITATIONS CONT
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Even if there are disparities between observed
and simulated yields, the simulated differences
could still truthfully reflect the observed
differences and therefore the impacts in
magnitude.
Also in the assessment of adaptation options, it
is the differences between pre and post adoption
yields and production that are taken into
account. In other words, model performance
could be adjudged satisfactory once the model
can truthfully indicate such differences, not
necessarily the actual productivity or production.
PERFORMANCE OF EPIC
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Epic is sensitive to plant environment factors in
general and specifically to climate factors
including: rainfall, solar radiation and
temperature.
It is demonstrated that the model could be
satisfactorily employed in the assessments of
impacts of and adaptations to climate variability
and climate change.
PERFORMANCE CONT
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It is also demonstrated that the model
could in a limited way, be employed in
assessing vulnerability and in estimating
crop productivity and production.
However the validity of the model output
need to be improved with calibration
based on potential heat units and choice
of evaporation-transpiration equations.
YIELD AND RAINFALL
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There is no significant relationship between yield
and seasonal weather forecast categories based
on total rainy season rainfall
Yield during a very wet year may be lower than
yield during a dry year
The current seasonal weather forecasts give
little indication as to what yield of crops may be
Climate has negative impacts not during a
normal dry year, but during an abnormally very
dry year, that is during an extremely dry year
Above observations are more characteristic of
the more humid southern area.
YIELD FORECASTS BASED ON
QUINT CATEGORIESRI tons/ha
Crop
V. wet
Wet
Avrage Dry
V. dry
Maize
0.62
0.60
0.57
0.54
0.50
G.Corn 0.59
0.59
0.49
0.51
0.46
Millet
0.14
0.15
0.12
0.12
0.11
Rice
0.24
0.26
0.16
0.16
0.16
Correlation: crop and rainfall
Confidence levels: 99**, 95*
Rainfall
Maize
Sorghum Millet
Growing
period
First
month
First two
months
Days
0.7759*
0.7121*
0.7633*
0.0948
o.1144
0.1084
0.8696** 0.8445** 0.8666**
0.2622
0.1420
0.3095
YIELD OF MAIZE AND
PLANTING DATES
Planting dates
March 16th
April 1st
April 16th
May 1st
May 16th
June 1st
June 16th
July 1st
Rainfall mm
412
488
507
532
531
525
515
505
Yield tons/ha
3.67
3.52
3.55
3.63
2.73
2.82
2.49
2.75
WEATHER FORECASTING
SKILLS
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NOAA and the Nigerian CFO demonstrate
higher forecasting skills than CNRS and UKMO.
The higher the resolution of the predictor SSTA
variable, the better the skill
The higher the no of predictor variables, the
better the skill
Significant differences are observed between
the forecasting skills demonstrated for each year
WEATHER FORECASTING
SKILLS CONT.
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There are regional disparities in the weather
forecasting skills. Higher skills observed for
southern wetter zones than for the northern
Sahelian zones.
Quint category forecasts prove not to be very
useful for crop yield forecasts.
Especially in the humid zones, years classified
as dry and very dry quint categories are not
necessarily bad for agricultuture.