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
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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.
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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
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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
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• 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
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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.
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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
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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
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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
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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
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• 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:
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• 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
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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
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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
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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
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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.
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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
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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.
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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
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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
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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
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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
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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
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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)
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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.
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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
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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
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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
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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