1 AF23 SECOND SEMI ANNUAL PROGRESS REPORT A SUMMARY During the period from July to December 2002, we have been able to advance the work on AF23 on several fronts including: data collection, preparation of papers for publication, conduct of field surveys and continued assessment of the research tools including the Crop Model and a number of a-priori hypotheses. With respect to data collection, we have been able to secure all available data on daily weather in Nigeria. During the next eight months, we intend to store them in our archives in both electronic and paper forms. Already, the data have been used to create about 1,050 Epic Daily Weather files, which are to be used in running the model to generate data on the impacts of climate variability. We have also adopted and downscaled data from IPCC’S Data Distribution Centre for the purpose of creating our own Climate Change Scenarios for the century. Field surveys, designed to highlight the human dimensions of the problem were conducted at two locations, one in the forest zone and the other in an area of Southern Guinea Savanna. We also continued our seminars based on existing literature in the various cognate disciplines. We have debated and come to an understanding on the usage of such terms as impacts, vulnerability and adaptive capacity as they apply to our research objectives. B TASKS PERFORMED AND OUTPUTS PRODUCED Data Collection During the period covered by the report, we were able to collect from the Nigerian Meteorological Agency all the daily weather data available from 1900 to 2000. These were in respect of 42 weather stations. The research project was designed with the hope that a more versatile version of MAGICC-SCENGEN would be available in the months following the publication of the Third Assessment Report. If what the authors promised had been realized, we would have had climate scenario data covering the century at a resolution of 0.5 x 0.5 degrees latitude and longitude; and for any time slice of choice. It now appears that the promised version is not likely to be available early enough for our purpose. We have therefore decided to use data available at the IPCC Data Distribution Center, downscaled by statistical and other empirical methods. We have perfected a method and started the process of data extraction. We have adopted Hadley M2, Members 1, 2, 3,4 and Total, and scenarios assuming 1 % and 0.5 % annual increases in CO 2 equivalents. We are constructing climate scenarios with the following time slices: 1961 – 1990; 2010 – 2039; 2040 – 2069; and 2069 – 2099. Methodological Issues The major methodological concerns at present relate to: • Performance of Epic Crop Model under tropical West African conditions • The skill level of the weather forecasting tools available for the region of interest, that is, West Africa, and • Downscaling potential climate change scenarios from the outputs of GCM experiments. AF23
A SUMMARY
During the period from July to December 2002, we have been able to
advance the work on AF23 on several fronts including: data
collection, preparation of papers for publication, conduct of field
surveys and continued assessment of the research tools including
the Crop Model and a number of a-priori hypotheses. With respect to
data collection, we have been able to secure all available data on
daily weather in Nigeria. During the next eight months, we intend
to store them in our archives in both electronic and paper forms.
Already, the data have been used to create about 1,050 Epic Daily
Weather files, which are to be used in running the model to
generate data on the impacts of climate variability. We have also
adopted and downscaled data from IPCC’S Data Distribution Centre
for the purpose of creating our own Climate Change Scenarios for
the century. Field surveys, designed to highlight the human
dimensions of the problem were conducted at two locations, one in
the forest zone and the other in an area of Southern Guinea
Savanna. We also continued our seminars based on existing
literature in the various cognate disciplines. We have debated and
come to an understanding on the usage of such terms as impacts,
vulnerability and adaptive capacity as they apply to our research
objectives.
B TASKS PERFORMED AND OUTPUTS PRODUCED
Data Collection
During the period covered by the report, we were able to collect
from the Nigerian Meteorological Agency all the daily weather data
available from 1900 to 2000. These were in respect of 42 weather
stations. The research project was designed with the hope that a
more versatile version of MAGICC-SCENGEN would be available in the
months following the publication of the Third Assessment Report. If
what the authors promised had been realized, we would have had
climate scenario data covering the century at a resolution of 0.5 x
0.5 degrees latitude and longitude; and for any time slice of
choice. It now appears that the promised version is not likely to
be available early enough for our purpose. We have therefore
decided to use data available at the IPCC Data Distribution Center,
downscaled by statistical and other empirical methods. We have
perfected a method and started the process of data extraction. We
have adopted Hadley M2, Members 1, 2, 3,4 and Total, and scenarios
assuming 1 % and 0.5 % annual increases in CO2 equivalents. We are
constructing climate scenarios with the following time slices: 1961
– 1990; 2010 – 2039; 2040 – 2069; and 2069 – 2099.
Methodological Issues
The major methodological concerns at present relate to: •
Performance of Epic Crop Model under tropical West African
conditions • The skill level of the weather forecasting tools
available for the region of interest,
that is, West Africa, and • Downscaling potential climate change
scenarios from the outputs of GCM
experiments. AF23
2
During the period July to December 2002, we employed considerable
time and effort in reviewing and testing our approaches to the
research. The project was approved for funding with the title
“Extended weather forecasts as a tool for the enhancement of crop
productivity in Sub Saharan West Africa”. The assumption was that
the forecasts would be sufficiently skillful to form the basis for
policy formulation. It is therefore logical at this stage to assess
the skills of the weather forecasting tools available for sub
continental West Africa. Available forecasting tools are products
of forecasting organizations in France, United Kingdom, West Africa
and United States of America. The weather forecasting tools are
experimental in nature and were primarily designed for application
in West Africa. The forecasts themselves are not presented with
sufficient detail to be used in devising strategies for the
enhancement of crop yields. Assessing the tools at the level of
detail at which they are made available indicates moderate
skills.
EPIC was designed for use in temperate latitude, continental United
States of America. It has been successfully applied in the study of
erosion, water pollution and crop growth and production. However,
in view of the major role the model is expected to play in the
assessments of impacts and adaptation strategies there is the need
to test its performance in an area with a different set of crops
and a different set of environmental conditions, that is tropical
sub- continental West Africa. Our main conclusion on the crop model
is that it could be satisfactorily employed in the assessments of
impacts of and adaptations to climate variability and climate
change. However, in assessing vulnerability and estimating crop
productivity and production, the model needs to be properly
calibrated for each site and each crop variety. Therefore, where
the objective is to estimate the amount of crop produced, the model
is best applied at farm level scale.
The paper under preparation is designed to use Geographical
Information System, specifically the Inverse Distance Weighting
(IDW) devise as method for downscaling from low resolution GCM
data. The approach admits data to an Arc View Theme through
low-resolution DDC data coordinates, interpolate grids using the
IDW devise in Arc View’s Spatial Analyst Extension, and retrieve
the downscaled version of the data in respect of points
representing a higher resolution field. We then go on to compare
the results with what has been obtained using other downscaling
approaches. Still on Geographical Information System, we have
created two Arc View models based respectively on the 19-state
structure of Nigeria (polygon) and 28 best-maintained weather
synoptic stations (point) for the assessment of impacts of climate
variability and climate change on crop production.
Creation of Epic Data Files
During the period covered by the report, as we promised during the
first semi annual report, we created Epic main data files for the
28 synoptic weather stations within Nigerian territorial space
using IIPC DDC observed data for 1961 – 1990. We have also
converted the daily weather information collected from the Nigerian
Meteorological Agency to Epic (Crop Model) Daily Weather files. The
latter are required for crop yield simulation, an essential first
step in the assessment of impacts of and vulnerabilities and
adaptations to climate change. With the daily weather files and the
main data files we conducted tests to demonstrate the sensitivity
of crop production systems to changes in weather and climate using
Epic Model.
AF23
3
Meetings and Seminars
During the period under review, we continued with our seminar
series on “Adaptation through Building Resilience in the
Agricultural Sector”. Among the topics considered are: ‘Removing
Biological Constraints’, ‘Removing soil and nutrient constraints’
and ‘Development of improved seeds: The role of Plant Breeding’. We
are making arrangements to edit the papers and present them as
occasional papers.
Initiation of Field Surveys
As the focus of the study shifts from purely biophysical to include
an appreciable human dimension, the importance of socio economic
field surveys increases. Field Surveys were conducted at selected
locations in the Forest and Southern Guinea Savanna Ecological
Zones. The Participatory Rural Appraisal (PRA) method was adopted
to collect information from the farmers. With the farmers, the
field team identified crops and cropping systems and attempted a
simple cost-benefit analysis of farm operations. Data were also
collected on the farmers’ experiences and reactions to extremes of
weather as they affect the profitability of farm operations.
Preliminary analysis of the data collected shows that forest
farmers cultivate mainly tree crops especially cocoa, kola nut, oil
palm and citrus. They also cultivate maize, yam, cassava and rice.
In the Southern Guinea Savanna, the main crops are maize, cassava,
yam, sorghum, rice, and millet. In the three ecosystems, cropping
systems include mono- cropping, double cropping and multiple
cropping.
In the forest, farm holdings range from one hectare to 25 hectares
while in the savanna they are from one hectare to 50 hectares.
However, the mean holdings per farmer are 7.0 ha, and 8.3 ha in the
forest, and Southern Guinea Savanna respectively, From the results
of the data collected, most of the peasant farmers do not keep
adequate records of their farm production. However, they gave
reliable estimate of their cost of production and the income
realized. Only a few of the farmers could recall past weather and
climatic events. In the Southern Guinea Savanna four farmers
recollected how early rains of 1972 resulted in bumper harvests
whereas only two farmers remembered the bad effects of late rains
of 1979. In the forest ecosystem only one farmer recalled the late
rain event of 1987 with its devastating effect on the tree
crops.
Student Participation
Six undergraduate and three graduate students are participating in
the project in the sense that the scopes of their respective
dissertations and theses are within the general theme of
‘consequences of climate variability and climate change’. The
doctorate student for example is working on climate change and
human security. One of the undergraduates is trying to use MAGICC –
SCENGEN to analyze potential climate change in Nigeria. We offer
some of the students vacation employment during which they work on
their projects. We also support the individual projects with
allowances to facilitate traveling to project areas of study for
fieldwork.
C) DIFFICULTIES ENCOUNTERED AF23
4
We had some problem with the crop model for which we admit full
responsibility. Our initial classification of the years into quint
categories of very wet, wet, average, dry and very dry was based on
annual total rainfall. The relationship of simulated crop yields to
these turned out to be very weak, implying an equally weak
sensitivity of the crop production system to climate variability.
However, when crop yields were correlated with the rainfall of the
period between planting and harvest days, coefficients significant
at 99 percent confidence levels were achieved. It may be argued
that this is indeed not a problem because we should have
anticipated the such weak relationships as described above, but it
slowed down the work for about three months while we were trying to
rectify what was supposedly wrong with the crop model.
D) CONTRIBUTIONS TO UNFCCC NATIONAL COMMUNICATIONS.
Nigeria lags behind in the preparation of the ‘First National
Communication’, which is yet to be published. The P.I. of AIACC
Project AF 23, Professor James Adejuwon, participated in two
workshops respectively in 1997 and 2000 designed to facilitate the
preparation of the Communication. During the first half of the year
2002, Professor Adejuwon was invited to edit a draft of the
Communication. It was quite obvious to him that much still needed
to be done before the project could be brought to a successful
conclusion. The main problem has to do with the absence of
personnel exposed to the IPCC assessment process in some critical
sectors, including the Science of Climate Change. Much work has
been done in the area of compilation of GHG emissions in the
country, but the absence of any work on Climate Change scenarios
means that works on impacts, adaptive capacities and vulnerability
will have to wait.
Professor Adejuwon has also been invited to participate in the
Sectoral Studies of Vulnerability and Adaptation to Climate Change
under the Canada – Nigeria Climate Change Capacity Development
Project. He is to coordinate the sectoral study on Agriculture,
Food Security, etc. The Nigerian Environmental Study/Action Team
(NEST), and Global Change Strategies International (GCSI) of Canada
are jointly implementing the project. Funded by Canadian
International Development Agency (CIDA), the project has the
approval of the Federal Ministry of Environment, which is a member
of the Project Management Team (PTM). One of the project’s four
activity areas is titled Vulnerability Assessment and Adaptation to
Climate Change. Five sectors including: Industry, agriculture and
food security, Wetlands, Marine ecosystems and Coastal Zone
Infrastructure, Human Health and Human settlements are listed for
consideration.
The terms of reference for the sectoral studies include the
following: • Characterize the level of sectoral vulnerability in
the context of current climate
conditions; • Assess the vulnerability of the sector under
different scenarios of climate change; • Document current
adaptation strategies (if any) at the individual, household,
community and national levels; • Suggest necessary adaptation
strategies and estimated costs of same, under
different scenarios of climate change at the various levels •
Document current and future gender implications of vulnerability
and adaptation
to climate change; AF23
5
• Document present and envisaged obstacles to adaptation to climate
change, under different conditions as they relate to different
population groups that may be at risk;
• Articulate and describe techniques, equipment and methodologies
for vulnerability assessment;
• Provide a brief on what you consider to be effective approaches
to vulnerability analysis and adaptation to climate change.
E) TASKS TO BE PERFORMED IN THE NEXT EIGHT-MONTH PERIOD
Impacts of Climate Variability
During the next eight-month period, our main objective is to
complete simulation exercises dealing with the impact of climate
variability on crop production. For each modeled site we shall run
the crop model for each year from 1961 to 1990. Model outputs will
be used to analyze impacts of climate variability on crop yields.
First we shall compute for each crop and each modeled site an index
of impact of climate variability on crop yield. At the moment we
are considering the coefficient of variability as a candidate for
this index. With this we can prepare maps of impacts to identify
the areas and the crops most seriously affected by the variability
of climate. We shall then go on to identify which parameters of
climate are responsible for the impacts. Our immediate objective
will be to present our results in form of publishable academic
papers
Continuation of Field Surveys
The other major task we intend to pursue during the next
eight-month period is field survey. During the next round of field
surveys, we shall focus on the northern, drier areas. We have
already made preliminary surveys to identify rural communities,
which shall form the targets of the surveys. As has been intimated
earlier, the surveys are intended for linking the biophysical
aspects of the research to the human dimensions of food security.
In particular, we will attempt to gather such information as are
required to mount a Cost-Benefit Analysis. The latter has been
adopted as a Decision Analysis Framework within which to make
judgments on such issues as magnitudes of impacts, thresholds of
disaster and what constitutes food security in the context of human
security.
Travels
During the next eight-month period, we intend to host Professors
Bill Easterling and Gregory Knight of the Pennsylvania State
University in Nigeria. They will be expected to discuss individual
problems with Nigeria based researchers, advise on methodologies
and peruse whatever draughts of publishable papers are available.
While Easterling will focus more on the crop model, Knight will be
expected to focus on the climate change scenarios. Depending on the
availability of funds, arrangements will be made for the visitors
to visit the sites of our field surveys. Also during the coming
eight-month period, the Principal Investigator will visit
Pennsylvania State University and the University of Columbia, New
York. The PI will attempt, during the visit, to discuss the
progress of the work with the USA based consultants and also with
Professor Cynthia Rosenzweig, an
AF23
6
AIACC mentor, specializing in agriculture and food security.
Another most important aspect of the visit will be literature
search and review, using the PSU Library.
We did secure AIACC funding for one of our students to visit
Columbia University in New York to learn about the IBNASAT Crop
Model. Up till now, the student has not been able to obtain the
necessary visa for the visit. We hope the visit will take place
within the next eight-month period.
Miscellaneous
• Preparation of more papers for publication • Supervision of
student projects • Description of Climate Change Scenarios using
MAGICC – SCENGEN models • Description of Climate Change Scenarios
using empirically downscaled IPCCC’S
DDC GCM Experiment data • Preparation of electronic and paper
editions of daily weather records for reference
purposes • Creation of Epic Data Files for 2010 – 2039; 2040 –
2069; and 2070 – 2099 time
slices
F) ANTICIPATED DIFFICULTIES IN THE NEXT EIGHT-MONTH PERIOD
We are not anticipating any difficulties in the next eight-month
period
G) ATTACHED PAPERS
Please find attached to this report a copy each of the completed
papers listed as follows: 1) Assessing the suitability of Epic Crop
Model for use in the study of
impacts of climate change on crop production in West Africa. 2)
Skill assessment of the existing capacity for extended weather
forecasting
in Sub Saharan West Africa.
7
AF23
AIACC REGIONAL STUDY EXPENSE REPORT Project statement of allocation
(budget), expenditure and balance (expressed in US$)
covering the period: 01 JULY 2002 – 30 DECEMBER 2002
Project Number: AIACC_AF23
Principal Investigator(s): JAMES OLADIPO ADEJUWON
Project Title: CLIMATE VARIABILITY, CLIMATE CHANGE AND FOOD
SECURITY IN SUB SAHARAN WEST AFRICA
Supporting Organizations: Global System for Analysis, Research and
Training (START), Third World Academy of Sciences (TWAS) United
Nations Environment Programme (UNEP)
I hereby certify that all information contained in this expense
report is true and correct.
Signed: ________________________________ Date: ________________
(Duly authorized official of administering institution)
Signed: ________________________________ Date: ________________
(Principal Investigator)
Signed: ________________________________ Date: ________________
(Principal Investigator)
AF23 BUDGET NARRATIVE
The rate of exchange used in the First Semi Annual Report was 134
local currencies to US$1.00. There has since been a change. The
rate applied in converting the second cash advance to local
currency was US$1.00 to 124.65. Because of this, there is no rate
applicable linking each currency to the other for the cumulative
expenses for the year 2002. Each column consists of additions of
the respective expenses for the two halves of the year rate.
Administrative charges for the University remains five percent of
each cash advance.
Sub contracting has been a very convenient way of executing the
technical aspect of the work such as the creation of data files by
computer analysts who are not listed as researchers or consultants.
Apart from the $4,000.00 vired to the SUB CONTRACT sub head in the
last report, we have taken the liberty again to vire the balances
from SUPPLIES AND EXPENCE, TELECOMMUNICATIONS, and COMPUTER
SERVICES to the SUB CONTRACT sub head.
Professor Olusegun Ekanade traveled extensively for reconnaissance
survey and location of potential field survey communities in the
forest and Southern Guinea Savanna zones. Dr Theo Odekunle was in
the headquarters of the Nigerian Meteorological Agency for the best
part of one month during which time he was able to extract data
dating from 1904 from hand written records. These explain the
expenditure on travels during the period from July to December
2002
Totals in the Table have been derived from the addition of the
amounts under the main (capitalized) heads. These include for
example: PERSONNEL, SUPPLIES AND EXPENSE, EQUIPMENT, etc.
9
AF23
Currency) (USD) (Local
Currency) (USD) (Local
Currency) (USD) (Local
Currency)
PERSONNEL 24,000 3,216,000 11,070 1,379,900 19,619 2,524,900 16,000
Lawrence Bamidele 1203 150,000 Olawale Adejuwon 2407 300,000
Adefunmike Ojo 481 60,000 Mary Omotayo 481 60,000 Angela Okonji 128
16,000 Yomi Adeyeye 321 40,000 Toyin Omonaiye 128 16,000 Maruf
Sanni 160 20,000 Sina Ayanlade 160 20,000 Principal Investigator
6,000 747,900 MATERIALS AND SUPPLIES 1,500 201,000 481 60,000 820
105,000 500 Stationeries Miscellaneous EQUIPMENT[2] 115,000
1,541,000 1,942 241,948 11,500 1,522,948 0 Desktop Computer 963
120,000 Photocopying machine 537 66,948 HP Scanjet 4400c Scanner
201 25,000 HP Deskjet 960C Printer 241 30,000 TRAVEL[3] 3,000
492,000 1,572 196,000 4,080 532,050 8,000 Olusegun Ekanade 1123
140,000 Theo Odekunle 449 56,000 CONSULTANTS[4] 10,000 1,340,000
6,417 800,000 11,268 1,450,000 6,000 Francis Adesina 802 100,000
Segun Ekanade 802 100,000 Theo Odekunle 401 50,000 Adekunle 401
50,000 Lat Gueye 401 50,000 Akin Farinde 802 100,000 Chinyere
Adeyemi 802 100,000 James Adejuwon 1,604 200,000 Felicia Akinyemi
401 50,000 SUB-CONTRACTS[5] 4,000 536,000 3,304 412,000 3,813
480,000 2,500
10
George Lewis Ltd 3,304 412,000 FIELD SURVEY 1,685 210,000 1,685
210,000 5,672 TELECOMMUNICATIONS 1,000 134,000 341 55,583 0
COMPUTER SERVICES 1,000 134,000 500 67,000 0 PUBLICATION COSTS
STUDENT PROJECTS 2,000 168,000 1,289 160,650 1,789 191,650 1,813
1,289 124,650 289 36,000 CONTINGENCIES 1,850 247,900 1,000 INDIRECT
COSTS 3,150 422,100 2,025 252,416 3,600 463,466 1,500 TOTAL 63,000
8,442,000 29,786 3,712,914 59,015 7,629,598 42,985
AF23
11
AF23 CASH ADVANCE INFORMATION AND REQUEST: (All figures should be
in US Dollars)
A. Amount of Previous Cash Advances: Date: April 2002 Amount:
$31,500.00
Date: Sept 2002 Amount: $40,500.00
Date: __________ Amount: __________
Date: __________ Amount: __________
Date: __________ Amount: __________
Date: __________ Amount: __________
TOTAL(1): $72,000.00
B. Expenditures (by Reporting Period)
Total Expenditures for Period 01 Jan 2002 – 30 Jun 2002:
$29,229.00
Total Expenditures for Period 01 Jul 2002 – 31 Dec 2002:
$29,786.00
Total Expenditures for Period 01 Jan 2003 – 30 Jun 2003:
____________________
Total Expenditures for Period 01 Jul 2003 – 31 Dec 2003:
____________________
Total Expenditures for Period 01 Jan 2004 – 30 Jun 2004:
____________________
Total Expenditures for Period 01 Jul 2004 – 31 Dec 2004:
____________________
TOTAL(2): $59,015.00
D. Total Estimated Expenses for Subsequent 8-Month Period:
$42,985.00 (from expense form)
E. Total Cash Advance Requested (D. minus C.): $30,000.00
12
AF23
PROJECT NUMBER: AF 23 PROJECT TITLE: Climate Variability, Climate
Change and Food Security in Sub Saharan West Africa. ADMINISTERING
INSTITUTION: OBAFEMI AWOLOWO UNUVERSITY, ILE-IFE, NIGERIA PRINCIPAL
INVESTIGATOR James Oladipo Adejuwon:
Description Serial No. Date of Purchase
Original Price (US$)
Present Condition
Location Remarks
Lap Top Computer 42872721 June 2002 2,388 New Dept of
Geography
Satisfactory
Satisfactory
Name: James Oladipo Adejuwon Signature:____________________Date:
_______________ (Principal Investigator)
1
ASSESSING THE SUITABILITY OF EPIC CROP MODEL FOR USE IN THE STUDY
OF IMPACTS OF CLIMATE CHANGE IN WEST AFRICA
James Adejuwon Department of Geography, Obafemi Awolowo University,
Ile-Ife, Nigeria
ABSTRACT
Scientists of the US Department of Agriculture developed EPIC Crop
Model. This paper tests its applicability for the assessment of
impacts of climate variability and climate change on crop
productivity in Sub Saharan West Africa. Among the crops whose
growth has been successfully simulated with Epic are six of West
Africa’s staple food crops: maize, millet, sorghum, rice cassava
and white yam. 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. It is
also demonstrated that the model could be successfully employed in
assessing vulnerability and estimating crop productivity and
production. However the validity of the model output could be
improved with calibration based on potential heat units and choice
of evaporation-transpiration equations. Key Words: Epic Crop Model;
Climate Change, Impacts, Vulnerability, Adaptations, Crop
Production, West Africa.
INTRODUCTION
Crop models are research tools usually applied in assessing the
relationship between crop production and environmental factors. The
more favored crop growth models currently in use are mostly plant
growth simulation models. These are mechanistic models that have
been shown to be efficient in determining the response of crop
plants to changes in weather and climate. Examples of such models
include EPIC (Williams et al, 1988), CERES (Ritchie et al, 1989),
GAPS (Butler and Riha, 1989), SOYGRO (Jones et al, 1989) and IBSNAT
(IBSNAT, 1989). In most cases these crop models have been developed
in particular localities and they are not always applicable in
every part of the earth’s surface without modification. Therefore
when introducing them into new regions, their applicability needs
to be evaluated. This paper is designed to assess the applicability
of EPIC (Erosion Productivity Impact Calculator; Williams et al,
1984, 1989) Crop Model for use in the study of impacts of climate
variability and climate change on crop productivity and production
in Sub Saharan West Africa.
Climate Change consequent upon increasing concentrations of Green
House Gasses in the atmosphere is among the more topical
contemporary environmental issues. The IPCC (Intergovernmental
Panel on Climate Change} in its Third Assessment report, has
demonstrated that it is no longer in doubt that global climate
changed dramatically during
2
the 20th Century, and that climate will continue to change more
precipitously in the coming centuries. This change will continue
irrespective of whether attempts at mitigation through
implementation of the Kyoto Protocol to the UNFCCC (United Nations
Framework Convention on Climate Change) are successful (IPCC,
2001). It had been concluded in the Second Assessment Report (IPCC,
1995) and reaffirmed in the Third Assessment Report that the
magnitude and direction of change in the various climate elements
will differ from one major region to the other. It was noted that
Climate Change could be beneficial in certain regions and
detrimental in other regions. It was observed that the less
developed countries and regions are likely to experience the worst
of the consequences of Climate Change partly because of negative
changes in water availability in the tropical regions and partly
because the communities concerned are poorly equipped to adapt. One
of the sectors that will be exposed to the potential negative
changes in climate is food production. It has therefore become
imperative, while trying to roll back climate change through the
implementation of the Kyoto Protocol, to formulate strategies for
living with a changed global climate.
ACQUISITION AND GENERAL FEATURES OF THE MODEL
EPIC was designed for use in continental United States of America.
It has been successfully applied in the study of erosion, water
pollution and crop growth and production. The newest version of
EPIC can be downloaded from www.tamu.edu at Blacklands Research
Station (Temple). Epic requires 446 items of input data; three
hundred of which are the climatic characteristics of each modeled
site. As downloaded from the web, the crop model comes with soil
and climate data that could be used to create program files for any
locality in the United States, including its associated islands.
For example, soil files in EPIC format for 709 soil series
representing a great majority of soils characterizing every part of
the USA are included in the downloaded package. Also included in
the package are comprehensive climate data for more than six
thousand weather stations. To load the climate data appropriate for
any site will not take more than a few seconds. The first problem
encountered in attempting to use EPIC for research in West Africa
is that such data as are necessary for creating program files for
experimental sites are not easily available. Where the primary data
are available, weeks and sometimes months of computation are needed
to convert them into the format required by Epic. The first version
of EPIC8120 we downloaded from the web could not respond when
latitudinal locations were set at 15 degrees or less. We had to
consult the originators of the Model (Jimmy Williams of USDA
Research Service) for trouble shooting the problem. While solving
the problem it was admitted that they had limited experience in
tropical environment and that the earlier versions might indeed
have problems in areas outside the USA. The replacement version and
the other versions subsequently downloaded responded normally. We
also had problem simulating cassava growth, which was similarly
attended to.
EPIC consists of a main data file created for each farm level site.
In the main part, the data file includes; program control codes,
general site data, water erosion data, climate data, wind erosion
data, soil data, management information operations codes,
management information operation variables, and operations
schedule. In the process of
3
creating the file for each site, the summary of the climatic
records of the station is entered. The climate parameters entered
include:
• Average monthly maximum air temperature, • Average monthly
minimum air temperature, • Monthly standard deviation of maximum
daily air temperature, • Monthly standard deviation of minimum
daily air temperature • Average monthly precipitation, • Monthly
standard deviation of daily precipitation, • Monthly skew
coefficient for daily precipitation, • Monthly probability of wet
day after dry day, • Monthly probability of wet day after wet day,
• Monthly maximum 0.5 hour rainfall, • Average monthly solar
radiation, • Monthly average relative humidity. • Monthly wind
velocity • Monthly velocity of wind from 16 cardinal points
The soil parameters include: bulk density, nitrogen content,
phosphorus content, clay content, sand content, soil water at field
capacity and soil depth from the surface in meters. The data are
provided in respect of the recognizable soil horizons. The
operations schedules identify the specific crops and include the
details of farm operations, such as timing, irrigation, pesticide
application, fertilizer application, tillage, sowing, crop density,
weeding, harvesting and the potential heat units. In the more
recent versions of EPIC, the operations schedule and the soil-
parameters have been pulled out of the main data file and created
into separate files.
EPIC uses nine other files to store and retrieve input data. All of
these files can be renamed, modified, or created by the user to
provide customized input data for specific applications. They can
be accessed and modified with UTIL or with any standard file
editor. These files include:
• The CROP PARAMETER FILE stores input data related to crop
characteristics
• The TILLAGE PARAMETER FILE stores information about tillage,
planting, and other equipment
• The PESTICIDE PARAMETER FILE provides data on pesticides used to
control insects and weeds
• The FERTILIZER PARAMETER FILE contains information on inorganic
and organic fertilizers
• The MISCELLANEOUS PARAMETER FILE stores miscellaneous data that
can be used to modify model sensitivity to a variety of
processes
• The GRAPHICS CONTROL FILE controls the parameters automatically
graphed as EPIC model is executed
• The MULTI-RUN FILE controls execution of multiple runs in which
the same operation schedule can be used for more than one
site
• The PRINT FILE specifies which output parameters will be printed
in the summary outputs
4
• The DAILY WEATHER FILE provides daily weather data that is read
by the model.
Experience shows that it is always less strenuous to create new
files by editing existing files. The model comes with the examples
of each of the necessary files. The first step in creating a new
file is to SAVE an existing file under a new name. Next, the
appropriate information about the new site is entered making sure
to run the new file several times before the editing is completed.
For example, to create a new soil file, first find all the required
data, and convert to the units of measurement stipulated by EPIC.
Next find, from EPIC archive, an existing file with the same number
of soil layers. Then replace the contents of the existing file with
the new set of data
While running EPIC, the main output variables of interest include
economic yield in kilograms/ha, biomass yield in kg/ha, water
stress in number of days of stress, temperature stress in number of
days, aeration stress in number of days, nitrogen stress in number
of days, and phosphorus stress in number of days. For each site and
each crop, the values of the output variables will vary according
to the operations schedule, soil, daily weather, planting dates,
level of fertilizer application, amount of irrigation water applied
among other factors.
MODEL SPECIFICATIONS
As noted earlier, Epic could be employed in various fields
including erosion control, pollution control, and hydrology, among
others. However, our interest in EPIC lies in its crop growth
component. The model is reputed to be able to simulate all crops
with one crop growth model using unique parameter values for each
crop. EPIC requires a number of crop-specific inputs. Once these
input parameter values are set for a crop species, they will not be
adjusted for individual data sets or locations. However, potential
heat units from planting to maturity may vary at different
locations for the same crop. The EPIC crop parameter table
presently contains parameters for about 95 crops. Among these are
five of the staple food crops of West Africa: cassava, maize,
sorghum, pearl millet and rice. Other crops found in our area of
study included in the list are: cotton, beans, groundnuts and
soybeans. Parameters for other crops may be obtained from experts
or from the literature.
In EPIC, biomass accumulation is primarily determined by light-use
efficiency constrained by a set of environmental factors among
which water-, temperature-, and nutrient-stress feature
prominently. Genetic factors inherent in the functions of the
individual crop plants are used to allocate primary biomass
production between above- and below- ground plant components. Crop
phenology, including the expansion of leaf area, is regulated by
the accumulation of heat units.
Warm temperatures accelerate phenological development of plants.
For example, high temperatures shorten times to shoot emergence, to
anthersis (pollen grain or tassel emergence in maize) and to grain
filling. Subtracting a crop specific base temperature from the
average daily temperature is the basis for deriving heat units.
Whenever the
5
average temperature is higher than the base temperature, heat units
accumulate. In EPIC, phenological development of the crop is based
on daily heat unit accumulation. Thus there is a given amount of
heat units required for the maturity of the crop. However,
potential growth is determined by the amount of photosynthetic
active solar radiation intercepted. The latter is a function of the
amount of leaf area provided by the crop in addition to foliage
characteristics, sun angle, row spacing, row direction and
latitude. In most crops, the leaf area is initially zero or very
small. It increases exponentially during early vegetative growth
when the rates of leaf primordial development, leaf appearance, and
blade expansion are linear functions of heat unit accumulation.
Subsequently leaf area expansion declines and approaches zero at
physiological maturity. In EPIC, an approach first suggested by
Monteith (1977) is used to estimate daily increases in biomass
using a parameter for converting energy to biomass.
Potential crop growth and yield are usually not realized because of
constraints imposed by the crop plant environment. EPIC estimates
stresses caused by water, nutrients, temperature, aeration, and
radiation. Parameter measures of these stresses range from 0.0 to
1.0. The stresses affect the crops in several ways. In the model,
the stresses are considered in estimating constraints on biomass
accumulation, root growth, and yield. The biomass constraint is
exercised by the minimum of water, nutrient, temperature and
aeration stresses. The root growth constraint is the minimum of
soil strength, temperature and aluminum toxicity. The potential
biomass predicted by Monteith’s equation is adjusted daily if any
of the five plant stress factors is less than 1.0. The water stress
factor is computed by considering the supply and demand of moisture
in the environment. On the other hand temperature stress is a
non-lineal function of the average soil surface temperature, the
base temperature and the optimum temperature for the specific crop.
The nitrogen and the phosphorus stress factors are based on the
ratio of simulated plant nitrogen and phosphorus contents to the
optimal values. When soil water content approaches saturation,
plants may suffer from aeration stress. The water content of the
top 1 m of soil is considered in estimating the degree of
stress.
Systematically, the model
• Estimates gross biomass productivity on the basis of light use
efficiency in the process of photosynthesis,
• Computes net biomass productivity by subtracting biomass consumed
in respiration from the gross biomass productivity,
• Allocates net biomass between root and above ground biomass. •
Determines the proportion of the above ground biomass that goes
to
yield, whether in form of grain, seed or tuber. • Applies indices
representing the constraining factors to reduce
potential yield to actual yield, • Uses a harvest index to compute
the harvest in terms of kg/hectare.
APPLICATION OF THE MODEL
There are five different ways in which Epic Crop Model could be
employed. These include:
6
• Estimation of crop productivity that is the yield of the crop per
unit area of land planted to it;
• Estimation of total crop production within a given land area or
territory; • Assessment of the impacts of climate variability and
climate change on crop
yields and crop production; • Assessment of the vulnerability of
crop production systems to climate variability
and climate change; • Assessment of adaptation options and
strategies for managing the negative
impacts of climate variability and climate change.
Crop productivity is the economic yield usually expressed as yield
per hectare. It can be estimated for any unit area, starting from
plots less than one hectare, and going up to local government
areas, states within a country, nation states and major world
regions. Yield is a measure of performance of the crop plant,
enhanced by favorable environmental factors and reduced by
constraining factors. Yield or productivity is the basic input for
the computation of production and the assessments of impacts,
vulnerabilities and adaptation options. For a crop model to be
useful in estimating productivity, model output needs to be
credible substitute for observed yield.
Crop production is simply the total amount of seeds, grain or tuber
for which a unit area is responsible. For a country or state within
the country, production figures represent the total farm output
from all the farm units in the country or state. As in the case of
productivity, the usefulness of a model in estimating production
depends on the extent to which the model output could be used as a
substitute for observed production. In other words, yields per
hectare from model output multiplied by area harvested must yield
results close to the production figures as observed on individual
farm plots and added up for geographical units up to nation states
and the world as a whole.
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. Impact is measured as the difference between the
situation before and after the environmental change occurs. In the
specific case of the impact of climate change or variability on
crop production, the impact is the difference between observed
yield before and after the change or variation in climate occurs.
The crop model allows us to hold constant all crop environment
factors while changing the climatic factor. To simulate the yield
of a crop for a given year, the daily weather file for that year is
used. This file is withdrawn and replaced in order to simulate the
yield of another year. Thus any change (in the yield) from one run
of the model to the other can be logically ascribed to differences
in the climate of the two years concerned. The changes observed in
the yields between two runs of the model can be interpreted as the
consequence or impact of changes in climate on crop variability or
crop production.
Vulnerability expresses the probability that a human or a
biophysical system falls into a state of disaster as a result of
environmental changes. In this study, the system of interest is
staple crop production while the environmental change of concern
relates to climate. The threshold to disaster is estimated as the
point in the changing environment at which crop failure occurs.
Crop failure can be defined in various ways. One of such ways
7
adopts the technique of Cost-Benefit Analysis. The crop is assumed
to have failed at the point at which the value of the farm output
is less than the costs of production. Vulnerability indices are
computed as the probability that the attempt to grow a particular
crop ends in crop failure. To arrive at these indices for specific
locations, attempts are made to simulate crop yields with EPIC over
a given period, for example, 1961-2000. From the results,
vulnerability indices are derived by converting the fraction of the
total number of years during which crop failure occurs to a
probability function.
Adaptations are the adjustments, which have to be made to crop
production systems in order to live successfully with a changed
climate. The probable adaptive responses are not new. They include
such farm level practices as: change of planting dates, adoption of
water conservation practices, change to early maturing varieties to
mitigate shortened growing season, change to drought tolerant crop
varieties, and change to high yielding crop varieties to take
advantage of unusually favorable weather. Other adaptation
strategies include: application of irrigation and adoption of
multiple cropping to take advantage of longer growing
seasons.
Policy makers require an assessment of the benefits derivable from
the adoption of the various adaptation options. Computation of such
benefits would require knowledge of the pre- and the post- adoption
yields in addition to the costs of the adaptation itself. In
addition, a comparison of the net benefits derivable from the
various adaptation options would be useful in making the choice
among potential adaptation options. Some of the potential options
cannot be integrated into EPIC. In such cases the crop model cannot
be effectively employed in the assessment. However, in cases
involving farm practices, such as irrigation, change of planting
dates crop substitution, multiple cropping, application of
fertilizers, which can be incorporated into EPIC, the model will be
extremely useful in assessing adaptation options
In general, to be able to successfully estimate crop production and
productivity, model output must be a credible substitute for
observed yields and observed production. In assessing
vulnerability, the model must be capable of accurately estimating
yields corresponding to various annual weather patterns and
specifically the yields for the year when the climate is at a
threshold between crop success or crop failure. What is needed for
the assessment of impacts of climate variability is the difference
between pre impact and post impact productivity and production.
Even if there are disparities between observed and simulated
yields, the simulated differences could still truthfully reflect
the observed differences 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.
MODEL TESTING
For application in our area of study, a two-stage approach has been
adopted to evaluate EPIC. The first stage consists of sensitivity
analysis, while the second stage consists of validation. In
sensitivity analysis, changes in model output consequent upon
changes in
8
environmental factors are evaluated. The environmental changes may
be arbitrary or may consist of real world observations. The
evaluation will show whether model output is justified by the
changes in the environmental factors. For example, application of
fertilizer is expected to result in increased yields. Sensitivity
is confirmed and model performance rated high when the model is
successfully employed to demonstrate this. In other words,
sensitivity analysis helps to determine whether the crop model can
be used to test an a-priori hypothesis. Validation on the other
hand involves the comparison of model predictions with real world
observations. Where for the same crop, the same location and the
same growing season model output and field measurements give the
same results in terms of yields, model performance is adjudged to
be high. Validation represents a more vigorous test of model
performance. Attempts to create more accurate and realistic data
files and thereby close the gaps between observations and
predictions are usually described as model calibration. Calibration
is a continuing exercise requiring contributions from users
especially in places other than where the model originated.
Sensitivity Analysis
Sensitivity to growing season rainfall
Moisture is a major factor in the environment of crops, especially
in a tropical location such as West Africa. The effectiveness of
EPIC as a tool in the main study will be determined to a large
extent by its capacity to demonstrate the sensitivity of the crop
production systems to seasonal rainfall parameters. In West Africa,
as in the other parts of the tropical world, the weather forecaster
is seldom asked what the temperature will be, but everyone is
greatly concerned about whether or not it is going to rain.
Normally, at elevations below 1000 meters, temperature never falls
below levels at which they could be stressful to crop plants. In
other words, the growing season lasts thermally, the whole year.
Temperature does not constitute a limiting factor on growth,
development or maturity of the crop plants. Thus it is moisture
rather than temperature that influences the abundance of natural
life. Life depends entirely on the amount of rainfall received and
so interest in climate or the weather naturally centers on the
amount, duration and distribution of rainfall. The crop plants are
sensitive to the moisture situation both during their growth,
development and especially as they reach maturity. This is
reflected in a definite soil and atmospheric moisture range in
which field preparations are expected to commence; and also in
which such farm operations as sowing, thinning, transplanting,
weeding, irrigation, insecticide and fertilizer applications as
well as harvesting are scheduled to take place. When soils are
either too wet or too dry, specific farm operations might prove
inefficient or harmful to growth and development. Dry spells within
the growing season could reduce economic yield considerably, or as
it often the case, result in total crop failure. On the other hand,
continuous rains could delay the harvest and expose yield to pest
damage. Moreover, since most crops require varying periods of time
for curing post-harvest, incessant rains could not only slow down
this process, but will also create a favorable environment for
moulds and fungi which may in turn cause a reduction in the quality
of the harvested crops.
For the analysis of sensitivity of the crop production systems to
seasonal rainfall, we have adopted climate and weather records for
Maiduguri to create an EPIC data file and run
9
the model for four crops over ten growing seasons from 1988 to
1999. The crops are: rice, millet, sorghum and maize. Maiduguri is
located in the Sahel zone in the north, eastern extremity of
Nigeria. For each year the crops are planted on the 1st day of June
and harvested on the 30th day of August. The outputs of the Epic
runs are depicted in Table 1. Also in the table are: the total
rainfall for the first month, the first two months the three months
and the number of rain days.
The driest year with respect to the total for the three growing
season months and the first two months was 1994. It is also the
year with the lowest yield for the four crops modeled. The year
with the next lowest yield for the four crops after1994 was 1992.
It is also the year with the lowest June-July rainfall, that is the
first two months after planting. At the other end of the moisture
regime, the wettest year, 1999, leads the other years in the yields
of maize, sorghum and pearl millet. The year, 1999, came third
among ten year in the simulated yield of rice. Table 2 depicts the
sensitivity of the yields of the various crops to rainfall
parameters in terms of correlation coefficients. The sensitivities
of the yields of maize, sorghum and millet to the rainfall of the
first two months after planting are demonstrated with values of r
significant at 99 percent confidence level. The corresponding
values of r for the relationships with the total rainfall from
planting to harvesting are significant at 98 percent confidence
limits.
However, it is not only in respect of rainfall that Epic Crop Model
can be used to demonstrate sensitivity. In the following
paragraphs, we show that sensitivity to temperature, radiation and
carbon dioxide concentration can also be demonstrated, using the
crop model.
Temperature
One way of testing model sensitivity to temperature is to increase
the minimum and maximum temperature for each of the growing season
months, that is: May, June, July August, and September. In Table 2,
along the ‘A’ row are depicted the outputs of Epic run with the
monthly mean minimum and monthly mean maximum temperatures based on
observed temperatures for 1961-1990. For row ‘B’, the monthly mean
maximum temperature is increased by 1oC, while the monthly mean
minimum temperature is increased by 2oC. For row ‘C’, both monthly
mean minimum and monthly mean maximum temperatures are increased by
2oC. Row ‘D’ shows outputs of runs made with 2oC increases in mean
monthly maximum temperatures and 3oC increases in mean monthly
minimum temperatures. The results indicate an increase in yield
corresponding to the increases in temperature. Epic therefore
demonstrates the sensitivity of maize crop production to increases
in temperature in terms of increases in the yields.
Solar Radiation
Solar radiation is the primary determinant of biomass yield from
which the other yields are derived. One would therefore expect
economic yields of crops to be related to the amount of incident
solar radiation. Consideration of model sensitivity to this factor
is
10
called for when attempting to adapt a model developed for temperate
latitude environment to a tropical region. One of the basic
climatic differences between temperate and tropical environments is
the much greater amounts of solar radiation received in the latter.
Climate change projections by the various Global Climate Models for
West Africa are for a higher level of solar radiation as a
consequence of lower levels of cloud cover. Decreases in cloud
cover with respect to the 1961-90 mean are projected to continue to
the end of the 21st Century. (IPCC, 2001)
Table 4 depicts the pattern of response of maize to different
levels of solar radiation, according to EPIC, at a site
corresponding to the weather station in Joss north central Nigeria.
In Nigeria, higher levels of solar radiation normally characterize
the earlier parts of the rainy season and the drier areas in the
north. This has been noticed in real life experiments and confirmed
by Epic simulation runs already conducted in the course of the
current exercise.
Carbon dioxide
Carbon dioxide and water are the main feedstocks for the processes
of primary production, that is photosynthesis, upon which life on
the earth’s surface ultimately depends. Carbon dioxide input into
photosynthesis comes from the atmosphere. One would expect an
enhanced level of carbon dioxide concentration in the atmosphere to
increase the gradient between the external air and the air spaces
inside the leaves, thus promoting higher levels of diffusive
transfer and absorption of CO2 into the chloroplasts and higher
levels of biological productivity. Higher levels of atmospheric
carbon dioxide also induce plants to be more economical in the use
of water. Thus with higher concentrations of carbon dioxide crops
may be less subject to water stress in areas normally considered
marginal with respect to precipitation. However, plant species vary
in their response to CO2 partly because of differing photosynthetic
mechanisms. Maize (corn), sorghum, sugarcane, and millet belong to
a physiological class (called C4 plants) that responds positively
to increased CO2 levels. On the other hand, wheat, rice, and
soybeans are C3 plants, which tend to be less responsive to
enriched carbon dioxide concentrations. The sensitivity of maize to
changes in the atmospheric concentration of carbon dioxide is
depicted in Table 4. The location used is Joss in Central Nigeria.
For each trial, the crop was planted on the first of June during a
year with very high growing season rainfall. Between concentrations
of 350 and 650 pip, productivity of maize rose from 2.607
tons/hectare to 2.871 tons /hectare.
Irrigation
Although this work is primarily an assessment of rain-fed
agriculture, irrigation as a very obvious management option needs
to be assessed. Therefore a consideration of the sensitivity of
Epic to irrigation is called for. Table 5 presents an analysis of
the sensitivity of the model to irrigation based on simulation runs
for a number of Nigerian sites. The difference between rain-fed and
irrigated production is more pronounced in Maiduguri located in the
Sahel ecological zone. This is quite understandable given the
endemic aridity. Irrigation resulted in more than double the yield
under rain-fed conditions.
11
Similar results were also observed at Ibadan located at the edge of
the rain forest. The rainy season is not always well established by
April 1st in Ibadan; therefore planting maize on that date may
expose the crop to considerable water stress. By June 1st in Jos,
and April 1st in Benin City respectively, when the rainy season had
become well established, maize could be produced without the need
for irrigation. Hence the very limited difference in yield between
rain-fed and irrigated production of the crop at these
locations.
The crop model can also be used to demonstrate sensitivity of
yields to other biophysical elements of the environment as well as
to crop genetics. In the following paragraphs, we present
sensitivity of crop yields to soil and crop variety as demonstrated
by model outputs.
Soil Sensitivity to soil is illustrated in Table 6. Soil
sensitivity analyses were conducted in respect of soils derived
respectively from igneous and metamorphic rocks (Ibadan), Eocene
sandstones (Benin), and recent lava flows (Jos). At each site,
maize growth simulation was conducted on contrasting soil types and
the yields recorded as depicted in Table 6.
The three soil types used for the Ibadan site belong respectively
to Iwo series, Osun series and Apomu series. Iwo is described as
clayey, while Osun is poorly drained and Apomu is sandy. The
interpretation of clayeyness in this as in as in the other cases is
that sandy clay texture is attained within 25 to 20 cm depth.
Normally the soils tend to be sandy near the surface and become
heavier with depth. For very sandy soils the texture may not attain
the sandy clay texture within the root zone of field crops. Thus
while Apomu and Osun are respectively characterized by growth
constraining features, (sandiness and water logging), Iwo series
with their loamy texture, and large reserves of weatherable
minerals have no such constraints. The respective yields of 3.739,
3.049, and 1.689 tonnes per hectare therefore conform to
expectations. Because the planting date was April 1st, at the
beginning of the rainy season, poor drainage proved to be less of a
constraint than anticipated. Hence the relatively high yield
recorded for Osun series.
Soils derived from recent lava flows are usually characterized by
relatively high fertility status. The basic minerals in the rocks
tend to bequeath to the soils derived from them favorable pH and
cation exchange capacity. This level of fertility is reflected in
the high yield of maize on the two soil types. Notwithstanding the
high yields recorded on both soil types, there are still some
differences noticeable in the yields based on texture
differentials. With respective yields of 5.205 and 4.070 tonnes per
hectare gwacl, the clayey series is substantially more productive
than gwasd, the sandy series.
The three soil series derived from Eocene Sandstone are deep, well
drained and strongly leached. Alagba is the very clayey series.
Sandy clay texture could be observed within 10 centimeters of the
surface in the more clayey variants. Agege is also clayey with
sandy clay texture attained within 40 centimeters of the surface.
However, the main difference between the two soil series is in the
lower horizons. While Alagba maintains evidence of
12
good drainage down to 3 meters, the lower horizons of Agege are
characterized by mottled clay. This is indicative of seasonal rise
and fall of the water table within these zones. Kulfo series, on
the other hand, consists of deep sands with sandy clay texture
attained at levels lower than 85 centimeters. Therefore in the
order of suitability for cropping, Alagba comes first followed by
Agege and Kulfo. Table 6 reflects this order of suitability in the
yield of maize. Planting was done on the first day of April when
the rainy season was already more than one month old at Benin. This
is what is responsible for the generally high yield. As expected,
the highest yield of 5.906 tonnes per hectare was achieved on
Alagba followed by 4.011 tonnes on Agege and 3.306 tonnes on Kulfo.
One lesson that must be learnt from the soil sensitivity analyses
is the substantial differences in yield that could result by
changing from one soil type to another when the weather situation
remains the same. Therefore while simulating yields in a locality,
efforts must be geared to using the more common soils types.
Crop Variety
One of the adaptation strategies that could be employed in
enhancing productivity in the face of variable climate is the
substitution of one crop variety for another. For example,
specially bred drought resistant or tolerant varieties could be
used for drought-prone locations or for forecasted years of
sub-normal rainfall. Early maturing varieties could be adopted when
a shorter than normal growing season is forecasted. To assess the
suitability of such varieties a crop model will be useful. Three
varieties of maize came with one of the EPIC8120 versions. These
were used to run the model for a number of locations in Nigeria.
The results are presented in Table 7. Planting dates at each
location is selected to avoid the earlier parts of the rainy season
when there could be inadequate rainfall. For all the stations in
the forest belt, maize was planted on the 1st of April, and for the
sites in the drier areas, planting took place on the 1st of June.
The first and most important observation is that Epic recognizes
the different maize varieties as indicated in the significant
differences in their yields. Average yield varies from 2.4 tons per
hectare for M1, to 0.3 tonnes per hectare for M2 and 1.2 tonnes per
hectare for M3. Second there are significant differences in the
yields realized for the same variety at different locations. This
can be interpreted as evidence that the varieties are sensitive to
site-specific factors of the plant environment. These results also
suggest that each crop variety needs to be separately modeled. The
disparities in yield could also be interpreted to mean that M1 is
adapted to the environment in Nigeria while M2 and M3 are not
Validation
Once the crop model is adjudged capable of demonstrating
sensitivity of the crop plant to climate variability, the next
exercise in testing the model is validation. Validation seeks to
establish the reliability of the outputs as possible substitutes
for observed data in estimating production and assessing
vulnerability. In the process of validation, observed yields of
crops are compared with the model outputs for the same crop, the
same sites and the same period. Ideally, for the model outputs to
be considered reliable for the stated objectives, model outputs
must be reasonably close to the observed yields.
13
Validation using the results of the 1986 Maize trial
Experiments
Table 9 depicts the grain yields in tons/ha of early maturing, open
pollinated maize varieties in the 1986 nationally coordinated
trials in Nigeria, under the auspices of the International
Institute for Tropical Agriculture (IITA). The 15 varieties
included in the table, consist of cultivars either being used or
being developed for adoption in the country. Observed yields in
respect of the varieties are presented from row 3 to. Row 18 gives
the average yield of the varieties per location, while row 19
depicts the coefficient of variation. The latter is an expression
of the standard deviation as a percentage of the mean yield of the
varieties. In row 20, we enter the yield simulated by Epic. In the
last row is depicted yield simulated by Epic as a percentage of the
mean yield of the varieties. The coefficients of variation among
the yields of the 15 maize varieties fall between 8 and 17 percent.
More important for validation purposes is the fact that Epic yields
simulated by Epic fall between 97 and 110 percent of the mean yield
of the varieties used in the trials. In other words, and it can be
observed from the table, simulated yields in all locations fall
within the bands set by the highest and the lowest observed variety
yields. What this implies is that these observations validate the
output of the Crop Model.
Validation with the results of the 1986 Rice Trial
Experiments
In Table 10 we present the results of the 1986 nationally
coordinated upland rice trials involving six varieties. There is
much contrast in yield among the varieties. In Ibadan, yields vary
from 0.8 tons per hectare to 3 tons per hectare. Average yield is
1.72 t/ha; standard deviation is 0.84, while the coefficient of
variation is 49 percent. At Ikenne, coefficient of variation is 17
percent while the average is 1.38. At Onne, yields vary between
1.18 ton/ha and 3.07/ha with a coefficient of variation of27
percent. At the three locations, EPIC yields fall between the bands
set by the lowest and the highest variety yields. However, EPIC
simulation yields were close to the variety averages only at Ibadan
(94%) and at Ikenne (112%). At Onne, Epic simulation yield was only
64 percent of the average for the varieties. Thus relatively
speaking, Epic yields were validated at Ibadan and Ikenne, but not
at Onne.
Calibration
There is always some gap between observed and simulated yields.
Differences between predictions and observations may result from
inaccuracies of the data used to run the model. Some of such
inaccuracies could be rectified while others could not be rectified
because of fundamental problems of measurement. Inaccuracies may
also result from the failure of the model to take account of all
the environmental factors as they change from one place to another.
Imperfections in the multiplicity of equations used to create the
model could add up to a substantial anomaly between simulated and
observed yields.
Calibration should start with ensuring that the model truthfully
reflects the determining environmental factors, the farm
operational schedules as well as the forms and the functions of the
crop plants. Environmental factors such as soils are not difficult
to
14
incorporate into the model even at the level of individual farms.
Soils samples could be taken from the farm sites, analyzed and the
results used to create epic soil files. However, the type of
climatic records required, are kept at very few locations. For
example, with a land area of nearly one million square kilometers,
there is less than 30 synoptic weather stations in Nigeria at which
the type of data needed are observed. Therefore models, in the best
of circumstances are constructed with climate data gathered some
distance away from the modeled sites. On peasant farmers plots,
there are standard practices over wide areas that could be
transformed into operations schedules. The main problem with
regards to this is that the model recognizes only mechanized
operations. The operation/tillage file does not include manual
tillage with hoes and clearing with machetes. For these the modeler
will have to adopt the nearest mechanized alternatives. In
conducting the validation exercise being reported here, we have
adopted the operations packages used on experimental farms by the
Institute of Agricultural Research and Training of the Obafemi
Awolowo University and The International Institute of Tropical
Agriculture.
Crop characteristics, however, cannot be fully truthfully reflected
in the model for the simple reason that the model usually comes
with a crop file that includes a single, unidentified variety,
whereas there are usually tens and in many cases, hundreds of
varieties of the same crop in real life situations. Some of the
varieties bear distinguishing characteristics, while most of them
cannot be separately identified either on the basis of form or
function. However, planted with the same operations schedule and
under the same environmental conditions, each variety is capable of
vastly different levels of yield. This notwithstanding, the
designers of Epic are not favorably disposed to users making
changes to the crop parameters unless such changes are based on the
results of rigorous experiments.
While conducting research, using archival observations, date of
planting and harvesting may not be known. In order to close the
gaps between observed and simulated yields in such a case,
modifications could be effected in the dates of planting. Such
modifications must be based on knowledge of practices in the area
of study. For example, early maize is usually planted immediately
following the third second or third heavy rain in the year. Going
through the records of the year of interest, one could easily
identify the probable date of planting.
Calibration could also be based on the length of the period from
planting to harvesting in cases where there are choices to be made
between early ripening and late ripening varieties. This may be the
reason why there is disparity between observed and simulated
yields. Losses due to pests and diseases and inefficient harvesting
technology could also create a difference between observed and
model yields. In general, one could assume that the observed yields
should always be lower than simulated yields. How much lower could
be resolved through special studies.
Making a choice between Evapotranspiration equations
15
There are other problems of validation, which relate to
uncertainties created by imperfect knowledge of environmental
factors and the way they affect crop yield. A very good example is
the rate of evaporation and transpiration. Epic comes with five ET
equations from which the modeler has to make a single choice for a
simulation exercise. The equations include: Penman-Monteith,
Penman, Prietley-Taylor, Hargreaves and Baier- Robertson. For the
same location, choice of potential ET equation could result in
significant differences in yield. Thus the modeler has to make a
choice based on research experience. Discovering what such a choice
should be is an exercise in model calibration.
To demonstrate the procedure for making a choice between the five
ET equations, we adopted yields of maize observed in an
agricultural experimental station, Ilora for 1996, 1997 and 1998
crop growing seasons. We then proceed to conduct five Epic runs,
adjusting the data file to make use of Penmann-Monteith’s,
Penman’s, Priestley-Taylor, Hargraves’ and Baier-Robertson’s
equations respectively. The observed varieties are early maturing;
the harvests were therefore set at 90 days after the planting. The
results are depicted in Table 10. In all cases, observed yields are
lower than simulated yields. Simulations run with Penman-Monteith
are nearest to the observed yields. As a percentage of simulated
yields, the observed yields of the Dmr.1sr.y variety varies from 66
for 1997, to 78 for 1998, and 91 for 1996. Sixty-six percent is
definitely too low to be accepted under any circumstance. The
corresponding percentages for the Suwan.1.sr variety are 83, 80,
and 89. The disparity of less than 20 percent of simulated yields
could be explained as harvest inefficiency. The outputs of the
model are closer to the yields observed with respect to the Suwan
variety. It is however noteworthy, that the order of magnitude of
the yields is from 1996, the highest to 1998 and 1997 with respect
to the two varieties observed and the yields simulated with
Penman-Monteith equation. The conclusion from the table is that
Penman-Monteith’s equation is the most appropriate to be used in
simulating maize yield at Ilora.
Calibrating Epic by changing Potential Heat Units (PHU)
Another parameter, which controls the magnitude of model yield, is
potential heat units. The heat units required for maturity of each
crop or crop variety at each site varies. For a crop such as maize,
realistic yields could be achieved by setting the PHU at values
between 1000 and 2000. However if a variety requires only 1200 PHU
and the model PHU is set at 1800, model output will differ from
observed output. In this case the process of calibration will have
to involve setting the PHU at the appropriate level.
In the USA, experiments conducted at various sites indicate that
PHU (potential heat unit) required for maturity by corn varies
between 1000 and 2,900. Several varieties were involved but the
emphasis was on site and geography. This provides the justification
for attempting to calibrate the crop model for use in Nigeria by
assigning various values to PHU on the Operations Schedule File.
The results are depicted in Table 12. For the three years,
simulated yields are lowest with PHU set at 1000 and highest when
it is set at 1800. Also for the three years simulations with PHU
set at 2000 give yields lower than those with PHU set at 1800. In
most cases, observed yields tend to be lower than EPIC
16
simulated yields. The exceptions are with respect to 1998 when the
observed yields are higher than simulated yields with PHU set at
2000. The observed yield for Suwan variety is also greater than the
simulated yield with PHU set at 1000.
Further analyses of the results of the simulation are presented in
Table 13a and 13b. The tables depict observed yields as percentages
of simulated yields. The results show that the skills of the
simulations are highest when PHU is set at 1000 and lowest with PU
at 1800.
Conclusions and Potential Performance of Epic
In conclusion, there is no doubt, going by the foregoing analyses,
that Epic is highly sensitive, not only to crop environment
factors, but specifically to such climate factors as moisture,
solar radiation, temperature and humidity. The model could
therefore be satisfactorily employed in the assessment of impacts
of and adaptations to climate variability and climate change.
However, in assessing vulnerability and estimating productivity and
production, the model needs to be properly calibrated especially
with regards to the potential heat units required. Also the model
must be capable of estimating the rate of water uptake by adopting
an appropriate evapotranspiration equation. Losses due to pests,
diseases and harvest inefficiency need also to be taken into
account. It must also be realized that within the same area,
different soils will result in vastly different yields and
production totals. Therefore, the model should be run not just with
one soil for each locality but also, with at least all the more
common soil types.
AWKNOWLEGEMENTS
This paper is written as part of the output of a research project
funded and supported by three organizations. The organizations are:
START (SysTem for Analysis, Research and Training), NOAA (USA
National Oceanic and Atmospheric Organization) and AIACC
(Assessment if Impacts of and Adaptations to Climate Change). The
supporting organizations for AIACC are: START, TWAS (Third World
Academy of Sciences) and UNEP (United Nations Environment
Programme).
REFERENCES.
Butler, I.W. and Riha, S.J. (1989) GAPS: A general Purpose
Formulation Model of the Soil – Plant – Atmosphere System Version
1.1 User’s Manual Department of Agronomy Cornell University, Ithaca
New York.
Hanks, R. J. (1983) “Yield and Water-use relationships: An
Overview” In H.M. Taylor, W.R. Jordan and T.R. Sinclair, Eds.
Limitations to Efficient Water use in Crop Production. pp. 393-411.
Amer. Soc. Agron., Crop Sci. Soc. Amer., Soil Sci. Soc. Amer.,
Madison, WI.
International Benchmark Sites Network for Agrotechnology Transfer
(IBSNAT) Project (1989): Decision Support System for Agro
technology Transfer Version 2.1 Department
17
of Agronomy and Soil Science, College of Tropical Agriculture and
Human Resources, University of Hawaii, Honolulu HI.
IITA (1988) IITA Maize Research Program, Annual Report,
International Institute of Tropical Agriculture, 1986
IITA (1988) IITA Rice Research Program, Annual Report,
International Institute of Tropical Agriculture, 1986
IPCC (1995) CLIMATE CHANGE 1995 Impacts, Adaptations and Mitigation
of Climate Change: Scientific-Technical Analysis Contribution of
Working Group II to the Second Assessment Report
IPCC (2001) CLIMATE CHANGE 2001 Impacts, Adaptations and
Vulnerability Contribution of Working Group II to the Third
Assessment Report
Jones, J.W; Boote, K.J; Hoogenboom, G; Jagtar, S. S; Wilkerson G.G.
(1989) SOYGRO V 5.42: Soybean Crop Growth Simulation Model: User’s
Guide; Department of Agricultural Engineering and Department of
Agronomy, University of Florida Gainesville.
Monteith, J.L., (1977): “ Climate and the efficiency of crop
production in Britain. “ Phil. Trans. Res. Soc. London B 281 pp.
277-329. Murdock, G. P., (1960) “Staple Subsistence Crops of
Africa” The Geographical Review vol. 50 pp. 523-40.
Ritchie, J. T., (1972) “A model for predicting evaporation from a
row crop with in complete cover.” Water Resources Res. Vol. 8; pp.
1205-1213.
Ritchie, J.T; Singh U; Godwin, D; and Hunt, L; (1989) A User’s
Guide to CERES – Maize V. 2.10; International Fertilizer
Development Center; Muscle Shoals.
Williams, J.R.; Jones, C.A; and Dyke, P.T. (1984): “A modeling
Approach to Determining the Relationship Between Erosion and Soil
Productivity” Transactions of the ASAE Vol 27, pp129-144,
Williams, J.R; Jones, C.A; Kiniry, J.R; and Spaniel, D.A; (1989):
“The EPIC Growth Model” Trans. Ame. Soc. Agric. Eng.
Table 1: Sensitivity of Crop production to rainfall in Maiduguri,
Nigeria
18
Year Jun-Jul- Aug Rain
1988 516 87 270 39 2.808 2.294 0.775 0.790
1989 422 88 202 31 2.574 2.301 0.688 0.957
1990 367 47 230 26 2.430 2.134 0.654 0.842
1991 385 90 181 32 2.503 2.184 0.709 0.945
1992 450 41 154 35 1.706 1.493 0.427 0.685
1993 373 19 223 24 2.184 2.011 0.559 0.870
1994 285 50 117 33 1.339 1.204 0.353 0.526
1996 431 58 254 35 2.209 1.989 0.607 0.824
1998 461 60 239 32 2.992 2.504 0.836 0.888
1999 554 24 368 38 3.483 2.789 1.006 0.929
Table 2: Correlation of Rainfall parameters with Crop yield based
on Table 1
19
Growing period rain 0.7759* 0.7121* 0.7633* 0.4560
First Month rain 0.0948 0.1144 0.1084 0.2029
First two months rain 0.8696** 0.8445** 0.8666** 0.5831+
No of rain days 0.2622 0.1420 0.3095 0.1655
** r is significant at 99 percent confidence level
* r is significant at 98 percent confidence level
+ r is significant at 90 percent confidence level
Table 3: Sensitivity of maize to temperature changes in Ibadan,
Western Nigeria
Yield tonnes/ha
WS (days) TS (days) NS (days) PS (days) AS (days)
A 2.607 1.6 9.6 0.0 0.0 0.0 B 3.998 2.1 4.6 0.0 0.0 0.0 C 4.384 3.0
3.5 0.0 0.0 0.0 D 4.865 2.4 2.5 0.0 0.0 0.0 A = mean min and mean
max temp 1970 – 1999 B = A max temp plus 1oC; A min temp plus 2oC C
= A max temp plus 2oC, A min temp plus 2oC D = A max temp plus 2oC,
A min temp plus 3oC
WS = water stress T S = Temperature stress NS = Nitrogen stress PS
= Phosphorus stress AS = Aeration stress
Table 4: Sensitivity of maize to different levels of solar
radiation in Joss, Nigeria Yield WS (days) TS (days) NS (days) PS
(days) AS (days)
20
Tones/ha A 2.607 1.6 9.6 0.0 0.0 0.0 B 2.759 1.5 9.7 0.0 0.0 0.0 C
2.904 1.6 9.7 0.0 0.0 0.0 D 3.062 1.3 9.8 0.0 0.0 0.0 A = mean
solar radiation 1961 – 99 WS = water stress B = A plus 5 percent TS
= temperature stress C = A plus 10 percent NS = nitrogen stress D =
A plus 15 percent PS = phosphorus stress
AS = aeration stress
Table 5: Sensitivity of maize to different levels of CO2
concentration in Joss, Nigeria
Yield tonnes/ha
WS (days) TS (days) NS (days) PS (days) AS (days)
350 ppm 2.607 1.6 9.6 0.0 0.0 0.0 370 ppm 2.687 1.6 9.6 0.0 0.0 0.0
500 ppm 2.835 1.7 9.6 0.0 0.0 0.0 650 ppm 2.871 1.7 9.6 0.0 0.0 0.0
WS = water stress TS = temperature stress NS = Nitrogen stress PS =
Phosphorus stress AS = Aeration stress
Table 6: Sensitivity of maize to irrigation in various locations
within Nigeria.
Locations Date of planting Rain-fed: yield of Maize
(tonnes/ha)
Irrigated: yield of Maize (tonnes/ha)
Maiduguri 1st June 2.302 5.614 Jos 1st June 3.708 3.879 Ibadan 1st
April 3.395 6.396 Benin 1st April 5.249 6.508
Table 7: Sensitivity to change of soil type in various locations
within Nigeria.
21
Location Parent rock Soil Series Yield/ha in tonnes Ibadan Igneous
Iwo 3.739 Ibadan Igneous Apomu 1.689 Ibadan Igneous Osun 3.047
Benin Sedimentary Alagba 5.906 Benin Sedimentary Agege 4.011 Benin
Sedimentary Kulfo 3.306 Jos Lava Gwacl 5.205 Jos Lava Gwasd
4.070
Table 8: Sensitivity to crop variety substitution (yields in tons
per hectare)
Locations Varieties of Maize Variety M1
Variety M2
Variety M3
Mean Yield
Coefficient of Var (%)
Ibadan 1.72 0.04 0.06 0.60 160 Benin 1.38 0.04 0.05 0.49 256 Lagos
0.86 0.02 0.03 0.30 193 Ilorin 1.87 0.26 0.97 1.03 0.78 Lokoja 6.47
1.67 6.46 4.87 0.57 Enugu 3.00 0.48 1.84 1.77 0.71 Calabar 6.67
1.62 6.39 4.89 0.58 P.H 3.15 0.36 1.35 1.62 0.87 Maiduguri 0.76
0.03 0.07 0.28 1.41 Bauchi 1.75 0.07 0.18 0.66 1.40 Jos 2.47 0.06
0.09 0.87 1.58 Kano 0.97 0.04 0.11 0.37 1.38 Kaduna 2.11 0.07 0.11.
0.78 1.47 Sokoto 0.93 0,03 0.06 0.34 150 Minna 2.12 0.07 0.17 0.78
149
22
Table 9 Grain Yield (tons/ha) of early maturing open-pollinated
maize varieties in the 1986 Nationally Coordinated maize trial at
locations in Nigeria {IITA Maize Research Programme 1986}
Stations-à Benin Ibadan Makurdi Kano Mokwa Varieties 8321-18 4.4
6.4 4.5 8.0 4.9 8321-21 3.7 5.6 4.9 6.8 6.8 8595-2 3.8 5.2 4.2 7.0
5.1 8505-3 3.5 5.2 4.6 7.0 5.4 8346-3 3.0 5.1 4.7 5.1 5.4 8322-13
3.9 5.1 4.6 6.4 5.2
8428-19 3.3 4.9 5.5 6.6 4.8 8505-9 3.0 4.4 4.4 5.8 5.6 TZB Gusau
3.5 3.9 5.3 5.3 4.2 8505-1 3.6 5.2 4.9 5.1 4.4 8338-1 3.3 4.2 4.8
5.4 4.7 8505-5 3.4 4.8 4.3 6.0 5.1 8326-18 3.7 3.8 4.3 4.8 4.5
EV8443SR 3.8 4.4 4.5 5.7 4.1 FE27WSR 3.0 4.0 4.1 4.2 4.1 Mean 3.5
4.8 4.6 5.9 5.0 Coeff var 11 15 8 17 14 EPIC 3.8 5.3 4.7 5.6 5.1
EPIC/Mean %
107 110 102 97 .102
Table 10 Grain Yield (tons/ha) of upland rice varieties at three
locations during 1986 wet season (IITA Rice Research Program;
Annual Report 1986)
Locations IBADAN IKENNE ONNE Varieties Tox 955-212-2-102 3 1.4 3.07
Tox 1854-02-2-2 2.17 1.61 2.48 Tox 955-208-12-101 1.87 1.55 2.53
ITA 235 (check) 0.81 1.29 2.46 ITA 257 (check) 0.8 0.97 1.18 OS6
(check) 1.69 1.48 2.17 Standard Deviation 0.84 0.23 0.62 Mean 1.72
1.38 2.31 Coefficient of Var 49 17 27 EPIC 1.62 1.55 1.47 Epic/Mean
% 94 112 64
23
OBSERVED YIELDS Varieties of maize
CROP MODEL OUTPUTS (Varying ET Equations)
Year Dmr.lsr.y Suwan.1.sr PenmanM Penman PriestleyT Hargraves
BaierR 1996 1.569 1.440 1.734 2.176 2.607 2.651 2.778 1997 1.022
1.246 1.549 1.870 2.314 1.697 2.184 1998 1.220 1.397 1.559 1.842
2.245 2.530 2.990
Table 12: Calibrating EPIC by altering the Potential Heat
Units
OBSERVED YIELDS (Varieties)
CROP MODEL OUTPUTS (Varying potential heat units)
Year Dmr.lsr.y Suwan.1.sr 1000 1200 1500 1800 2000 1996 1.569 1.440
1.491 1.734 1.965 2.069 1.885 1997 1.022 1.246 1.391 1.549 1.706
1.983 1.821 1998 1.220 1.397 1.260 1.559 1.824 2.159 1.018
Table 13a: Making a choice of PHU for Dmr variety: Percentage
difference between Observed and simulated yields
Percentage difference of observed from simulatedObserved Yield
Tons/ha Potential heat units
Year Dmr.1sr.y variety 1000 1200 1500 1800 2000 1996 1.569 105 90
80 76 83 1997 1.022 82 66 60 52 56 1998 1.220 97 78 67 57 119
Table 13b: Making a choice of PHU for Dmr Variety: Percentage
difference Percentage difference of observed from simulatedObserved
Yield
Tons/ha Potential heat units Year Suwan.1.sr var 1000 1200 1500
1800 2000 1996 1.440 96 83 73 70 76 1997 1.246 90 80 73 63 68 1998
1.397 110 90 90 65 137
24
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1
SKILL ASSESSMENT OF THE EXISTING CAPACITY FOR EXTENDED WEATHER
FORECASTING IN SUB SAHARAN WEST AFRICA
James Adejuwon Department of Geography Obafemi Awolowo University
Ile-Ife, Nigeria
Theophilus Odekunle Department of Geography Obafemi Awolowo
University Ile-Ife, Nigeria
Abstract The need for skillful weather forecasting as a strategy
for adapting food production to variable and changing climate is
recognized. Frequent assessment of the existing tools provides the
needed feedback to encourage the growth of more reliable weather
forecasting capacity. A scheme designed for the assessment of the
skills demonstrated by published weather forecasts is presented.
The existing products of four of the weather forecasting
organizations with interests in West Africa are assessed using the
observed weather during the period from 1996 to 2000. The weather
forecasting organizations concerned are: USA NOAA, United Kingdom
Meteorological Office, CNRS (France) and The Nigerian Central
Forecasting Office. The forecast skills of the various
organizations appear not to have witnessed any significant
improvement between 1996 and 2000. Overall the proportion of the
forecasts falling into the “low skill” category is not
discouraging. However the relatively high percentage of the
“moderate skill” and low percentage of the “hi