ASSESSMENT OF WEATHER VARIABILITY IMPACT ON CASSAVA YIELD IN SOUTH WESTERN NIGERIA
Abstract
The scientific evidence of rainfall and temperature variability with their significant impacts on crops yield is
now stronger than ever. It is even more so on cassava that serves as staple food in most parts of Southwestern
Nigeria. Hence, this study, aimed at using Kriging interpolation and other geospatial analysis techniques to
assess and map the spatiotemporal variation to investigate the relationship between the yield of cassava and
temperature and rainfall for the period 17 years (1991 – 2007) in South Western, Nigeria. Data for an annual
yield of cassava for all the time period were collected from the Federal Ministry of Agriculture, Nigeria,
while data on the weather (temperature and rainfall) in the Southwestern states of Nigeria were obtained
from the archives of the Nigerian Meteorological Services (NIMET) on an annual basis. ArcGIS (9.2
version) was used to analyze the annual temperature, rainfall and cassava yield data. Spatial variation maps
were generated using Kringing method of Interpolation. Existing Map of Nigeria in JP2 format is imported
into the ArcGIS software. The mean rainfall variation maps show the areas which cut cross across Lagos and
part of Ogun states have the highest mean rainfall value range of 134.3 – 137.3 cm while the area having
lowest mean rainfall value range of 107.6 – 110.6 cm. Mean cassava yield geospatial variation map show
areas with the highest cassava yield of 16.9 – 17.8 tones with areas with the lowest cassava yield of 9.6 – 10.4
tonnes. For mean temperature variation maps, the area which cross cut across through Ekiti and part of
Ondo states have the lowest mean temperature value range of 31.3 – 31.4 0 C while areas, mainly in Ogun
state has the highest mean temperature value range of 31.9 – 32.0
0 C. Mean cassava yield geospatial
variation map depicts the areas with the highest cassava yield of 16.9 – 17.8 tonnes, while the area with the
lowest cassava yields of 9.6 - 10.4 tones. The results from these assessment and analysis will help cassava
farmers so as to make better cassava farming plan despite the varying weather condition as to ensure smart
agriculture.
