INTRODUCTION One of the problems of development in Sub Saharan West Africa region of west Africa pertains to food insecurity. Research needs to address this both as a means of improving food productivity in the present and in the future when the climatic conditions maybe less favorable for agricultural purposes

THE CONTEXT What is potentially at stake providing the justification for this study is the social, cultural and economic development of West Africa, based on a sustainable use of the resources of the environment. It is not a subject for contention, that every human being is entitled to, and should have access to the fruits of development which include: adequate food, clean water and energy, safe shelter, a healthy home environment, qualitative education, and satisfying employment. However, notwithstanding the spectacular gains in the means of development, such as the advances in science, technology and medicine during the just concluded century, the process has been skewed to the detriment of certain major regions of the world. Sub Saharan West Africa is probably the least developed of the world ’ s major regions going by the statistics available at the end of the 20 th Century. Moreover the prospect for the type of accelerated development needed to bridge the gap between this region and the other regions is not bright.

FOOD INSECURITY 1 There has been an upward trend in national food production during the last decade. However, because of a high rate of population increase, food availability per capita has declined Compared with the developed countries, nutritional standard is still very low.

FOOD INSECURITY 2 Average national dietary energy deficit varies between 210 and 390 kg/person compared with 110 to 160 in the developed countries. With the exception of Nigeria, all countries received food aid in1999. From 1994 t0 1999, no country in the sub continent was self sufficient in cereal production.

CLIMATE VARIABILITY 1 Interannual variation in monthly temperature and radiation is very low compared with precipitation Coefficient of variation of monthly temperature and radiation is usually less than 5% Coefficient of variation of monthly precipitation can be as high as 500% Coefficient of variation of monthly precipitation is lowest during the wet season and in the wetter southern areas than in the north In essence climate variability means precipitation variability

CLIMATE VARIABILITY 2.  There has been a general trend towards aridity in most of the stations studied;  All the rainfall time series, when smoothed with the 5-year moving average, reveal patterns characterized by oscillations;  The fluctuations demonstrate some periodic tendencies which are regular in nature;  The fluctuations are also characterized by strong persistence and temporal dependencies;

CLIMATE VARIABILITY 3  There appears to be a general lack of correspondence in the patterns of the fluctuations between seasons. In other words, a wet March-April-May is not necessarily followed by a wet June-July-August.  Also, there appears to be regional variations in terms of the rainfall fluctuations. In other words, dry years in one region are not necessarily dry years in other regions.

CROP MODEL APPLICATIONS There are five different ways in which Epic Crop Model could be employed. These include: Estimation of productivity Estimation of total production Assessments of impacts of environment factors Assessment of vulnerability Assessment of adaptation options

DEFINITIONS Crop productivity is the economic yield usually expressed as yield per hectare. Crop production is simply the total amount of seeds, grain or tuber for which a unit area is responsible. Impact is the change observed in the form or function of a biophysical or human system as a result of a change in the environment.

DEFINITIONS CONT Vulnerability expresses the probability that a human or a biophysical system falls into a state of disaster as a result of environmental changes. Adaptations are the adjustments, which have to be made to crop production systems in order to live successfully with a changed climate.

LIMITATIONS OF CROP MODEL IN CROP-CLIMATE STUDIES To be able to successfully estimate crop production and productivity, model output must be a credible substitute for observed values. For vulnerability assessment, the model must be capable of accurately estimating yields corresponding to various annual weather patterns. What is needed for the assessment of impacts of climate variability is the difference between pre impact and post impact productivity and production.

LIMITATIONS CONT Even if there are disparities between observed and simulated yields, the simulated differences could still truthfully reflect the observed differences and therefore the impacts in magnitude. Also in the assessment of adaptation options, it is the differences between pre and post adoption yields and production that are taken into account. In other words, model performance could be adjudged satisfactory once the model can truthfully indicate such differences, not necessarily the actual productivity or production.

PERFORMANCE OF EPIC Epic is sensitive to plant environment factors in general and specifically to climate factors including: rainfall, solar radiation and temperature. It is demonstrated that the model could be satisfactorily employed in the assessments of impacts of and adaptations to climate variability and climate change.

PERFORMANCE CONT It is also demonstrated that the model could in a limited way, be employed in assessing vulnerability and in estimating crop productivity and production. However the validity of the model output need to be improved with calibration based on potential heat units and choice of evaporation-transpiration equations.

YIELD AND RAINFALL There is no significant relationship between yield and seasonal weather forecast categories based on total rainy season rainfall Yield during a very wet year may be lower than yield during a dry year The current seasonal weather forecasts give little indication as to what yield of crops may be Climate has negative impacts not during a normal dry year, but during an abnormally very dry year, that is during an extremely dry year Above observations are more characteristic of the more humid southern area.

YIELD FORECASTS BASED ON QUINT CATEGORIESRI tons/ha CropV. wetWetAvrageDryV. dry Maize G.Corn Millet Rice

Correlation: crop and rainfall Confidence levels: 99 **, 95 * RainfallMaizeSorghumMillet Growing period *0.7121*0.7633* First month o First two months **0.8445**0.8666** Days

YIELD OF MAIZE AND PLANTING DATES Planting datesRainfall mmYield tons/ha March 16 th April 1 st April 16 th May 1 st May 16 th June 1 st June 16 th July 1 st August 1st

WEATHER FORECASTING SKILLS NOAA and the Nigerian CFO demonstrate higher forecasting skills than CNRS and UKMO. The higher the resolution of the predictor SSTA variable, the better the skill The higher the no of predictor variables, the better the skill Significant differences are observed between the forecasting skills demonstrated for each year

WEATHER FORECASTING SKILLS CONT. There are regional disparities in the weather forecasting skills. Higher skills observed for southern wetter zones than for the northern Sahelian zones. Quint category forecasts prove not to be very useful for crop yield forecasts. Especially in the humid zones, years classified as dry and very dry quint categories are not necessarily bad for agricultuture.